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 .
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 1-3, 5-8, 15, and 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
Claims 1-3, 5-8, 15, and 17-18 are within the four statutory categories. However, as will be shown below, claims 1-3, 5-8, 15, and 17-18 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 17, and 18 are representative of the inventive concept and recite:
Claim 1
An information processing apparatus comprising:
at least one processor, wherein the processor is configured to:
acquire first medical document data including first information related to a patients wherein the first information is information to be recorded in a plurality of items included in the first medical document data;
derive, in a case where omission has occurred in at least an item of the first information, second information relating to a necessity of nursing care or assistance for the patient by using a trained model that is configured to:
receive the first information recorded in the items in which the omission has not occurred as an input,
and output the second information that is information to be recorded in at least one item included in second medical document data based on historical combinations of the first information and the corresponding second information;
and calculate an accuracy of the second information derived based on the first information recorded in the items in which the omission has not occurred;
generate the second medical document data including the derived second information;
and control presentation of the second medical document data based on the calculated accuracy, wherein the processor is configured to:
present, in a case where the accuracy is less than a threshold value, the second medical document data together with information which indicates the item of the first information in which the omission has occurred as omission information;
and present, in a case where the accuracy is equal to or greater than the threshold value, the second medical document data together with information which indicates a fact that the accuracy of the second information is relatively low as the omission information.
*Claims 17 and 18 recites similar limitations as claim 1, but for a method and non-transitory computer-readable storage medium.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
deriving, calculating, and generating) or using pen and paper. Other than reciting generic computer
components/functions such as “apparatus” and “processor”, nothing in the claims precludes
the highlighted portions from practically being performed in the mind. For example, in claim 1, but for
the system language, the claim encompasses the user collecting data from various sources and compiling the data into a document. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer
components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally,
the mere nominal recitation of a generic computer does not take the claim limitation out of the mental
process grouping. Thus, the claim recites a mental process. The recitation of generic
computer components/functions of presenting also covers behavioral or interactions between people (i.e. the system and processor), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions), hence the claim also falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 2-3, 5-8, and 15 recite additional subject matter which further narrows or defines the abstract idea embodied in the claim.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claims
recite the following additional limitations:
Claim 1 recites: “apparatus”, “processor”, “trained model that is trained through machine learning”, “input”, “output”, “acquire first medical document data including first information related to a patients wherein the first information is information to be recorded in a plurality of items included in the first medical document data”, “receive the first information recorded in the items in which the omission has not occurred as an input”, and “and output the second information that is information to be recorded in at least one item included in second medical document data based on historical combinations of the first information and the corresponding second information”.
In particular, the additional elements do not integrate the abstract idea into a practical application,
other than the abstract idea per se, because the additional elements amount to no more than limitations which:
Amount to mere instructions to apply an exception. The limitations are recited as being
performed by an “apparatus”, “processor”, and “trained model that is trained through machine learning”, which are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The “trained model that is trained through machine learning” is used to generally apply the abstract idea without limiting how the model functions. The model/machine learning is described at a high level such that it amounts to using a computer with a generic model to apply the abstract idea.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of apparatus”, “processor”, “trained model that is trained through machine learning”, “input”, “output”, “acquire first medical document data including first information related to a patients wherein the first information is information to be recorded in a plurality of items included in the first medical document data”, “receive the first information recorded in the items in which the omission has not occurred as an input”, and “and output the second information that is information to be recorded in at least one item included in second medical document data based on historical combinations of the first information and the corresponding second information”.
Dependent claims 2-3, 5-8 and 15 do not include any additional elements beyond those already
recited in independent claims 1, 17, and 18 and dependent claims 7 and 8, hence do not integrate the
aforementioned abstract idea into a particular application. 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 a computer or any
other technology. Their collective function merely provides conventional computer implementation and
do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 17, and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: An apparatus in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional
limitations, other than the abstract idea per se, amount to no more than limitations which amount to
elements that have been recognized as well-understood, routine, and conventional activity in particular
fields as demonstrated by the recitation of an additional element such as:
Presenting, which refers to the process of transforming complex, raw data into clear, understandable visual formats (TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional.
Acquiring, which refers to the process of collecting information from various sources (TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional.
Input, which refers to information entered into a system (Para 59, Verma(US 10721141 B1) discloses: “This conventional input can include, for example, a push button, touch pad, touch screen, wheel, joystick, keyboard, mouse, keypad, or any other such device or element whereby a user can input a command to the device.”) in a manner that would be well-understood, routine, and conventional.
Output, which refers to information produced and/or delivered by a system (Para 0282, Fedichev(US 20190365332 A1) discloses: “Examples of such embodiments include a purposely-designed wearable device, such as 4123 or 4131, equipped with a visual or audible or otherwise human-perceivable indication, as illustrated by 4142; a smartphone or a computer, such as 4121, 4122 or 4132, equipped with the software to report the wellness indication via conventional output devices (e.g. a display, a visual display, an audio output, a sound, a haptic output device, a brain computer interface, a vibration, or the like) attached to them; or a system comprising one or more of the apparatus for producing the estimations as described herein together with some remote server 4150, optionally equipped with the database to store the values of the estimations and a device 4141, for displaying the values of the estimations obtained from the said remote server.”) in a manner that would be well-understood, routine, and conventional.
Dependent claims 2-3, 5-8 and 15 do not include any additional elements beyond those
already recited in independent claims 1, 17, and 18 and dependent claims 7 and 8. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 17, and 18 hence do not amount to “significantly more” than the abstract idea. 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 function merely provide conventional computer implementation.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-8, 15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Mitsumori(US20200342997A1) in view of Minami(US20080069450A1), Miao(US20220367057A1), and
, and Iyengar(US20210357781A1).
Claim 1
Mitsumori discloses:
An information processing apparatus comprising: at least one processor, wherein the processor is configured to: acquire first medical document data including first information related to a patient(Figure 3, Mitsumori discloses “Findings”, which can be considered first information), wherein the first information to be recorded in a plurality of items included in the first medical document data; (Figure 3, Mitsumori discloses “Data Type”, which can represent second information based on first information) (Figure 6, Mitsumori discloses second medical document data which includes second information(P1)); and control presentation of the second medical document data(Para 0066, Mitsumori discloses the displaying of data can be predetermined)
Mitsumori does not explicitly disclose:
derive, in a case where omission has occurred in at least an item of the first information
the second information relating to a necessity of nursing care or assistance for the patient
by a trained model
recorded in the items in which the omission has not occurred as an input
and output the second information that is information to be recorded in at least one item included in the second medical document data based on historical combinations of the first information and the corresponding second information
and calculate an accuracy of the second information derived based on the first information recorded in the items in which the omission has not occurred
based on the calculated accuracy
wherein the processor is configured to: present, in a case where the accuracy is less than a threshold value
the second medical document data together with information which indicates the item of the first information in which the omission has occurred as omission information
and present, in a case where the accuracy is equal to or greater than the threshold value
the second medical document data together with information which indicates a fact that the accuracy of the second information is relatively low as the omission information
Minami discloses:
wherein the processor is configured to: present, in a case where the accuracy is less than a threshold value(Para 0159, Minami discloses: “When the ratio is greater than the ratio threshold value Ts (YES at S109), the control section 227 concludes the presence of entry omission (S110)”),
the second medical document data together with information which indicates the item of the first information in which the omission has occurred as omission information(Figure 19A, Minami discloses the omission (“item name”) and where the omission occurs within the first information (“ID1”));
and present, in a case where the accuracy is equal to or greater than the threshold value(Para 0159, Minami discloses: “When the ratio is greater than the ratio threshold value Ts (YES at S109), the control section 227 concludes the presence of entry omission (S110)”),
the second medical document data together with information which indicates a fact that the accuracy of the second information is relatively low as the omission information(Para 0244, Minami discloses: “entry omission (erroneous omission) in the document image can be determined with satisfactory accuracy. “).
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the medical information processing apparatus of Mitsumori to add omission has occurred in at least a part of a plurality of pieces of the first information used for deriving second information, where an accuracy of the second information derived based on the other part of the first information in which the omission has not occurred is less than a threshold value, the second medical document data together with information which indicates an item of the first information in which the omission has occurred as omission information, and present, in a case where the accuracy is equal to or greater than the threshold value, the second medical document data together with information which indicates a fact that the accuracy of the second information is relatively low as the omission information, as taught by Minami. One of ordinary skill would have been so motivated to provide a means to derive medical document data but also determine whether data is missing, to better enable a comprehensive view of the status of a patient, but in this case for a document reading apparatus (Para 0008, Minami discloses: “Accordingly, even a document image with omission in which necessary information is not written in the entry fields of the inputted document image can be determined as being similar to a stored format. This has caused a problem that in spite of the omission in the document image, the inputted document image is filed intact. Thus, it has been desired to determine omission in a document image with satisfactory accuracy specifically in the area of healthcare to enable improved patient outcomes.”).
Minami does not explicitly disclose:
derive, in a case where omission has occurred in at least an item of the first information
the second information relating to a necessity of nursing care or assistance for the patient
by a trained model
recorded in the items in which the omission has not occurred as an input
and output the second information that is information to be recorded in at least one item included in the second medical document data based on historical combinations of the first information and the corresponding second information
and calculate an accuracy of the second information derived based on the first information recorded in the items in which the omission has not occurred
based on the calculated accuracy
Miao discloses:
derive, in a case where omission has occurred in at least an item of the first information(Figure 1, #S100, Miao discloses acquiring datasets where data is missing),
the second information relating to a necessity of nursing care or assistance for the patient(Para 0005, Miao discloses medical monitoring data),
by a trained model(Para 0020, Miao discloses a trained generative adversarial network imputation model)
recorded in the items in which the omission has not occurred as an input(Figure 1, #S200, Miao discloses original data with no omission),
and output the second information that is information to be recorded in at least one item included in the second medical document data based on historical combinations of the first information and the corresponding second information(Figure 1, #S700, Miao discloses the imputation of missing data as an output based on #S200);
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the medical information processing apparatus of Mitsumori to add derive, in a case where omission has occurred in at least an item of the first information, the second information relating to a necessity of nursing care or assistance for the patient, trained model, recorded in the items in which the omission has not occurred as an input, and output the second information that is information to be recorded in at least one item included in the second medical document data based on historical combinations of the first information and the corresponding second information, as taught by Miao . One of ordinary skill would have been so motivated to provide a means to accurately derive omitted medical data from patient data, to potentially improve patient outcomes, but in this case for a missing medical data imputation method (Para 0006, Miao discloses: “The medical data missing will result in incomplete data information, which directly affects the later medical diagnosis. Therefore, it is necessary to impute missing data for the medical diagnosis data to improve the integrity of the data, thereby improving the quality of the data analysis in later medical diagnosis.”).
Miao does not explicitly disclose:
and calculate an accuracy of the second information derived based on the first information recorded in the items in which the omission has not occurred
based on the calculated accuracy
Iyengar discloses:
and calculate an accuracy of the second information derived based on the first information recorded in the items in which the omission has not occurred(Figure 1, #103, Iyengar discloses calculated the number of errors on an imputation, which can be considered the same as determining accuracy)
based on the calculated accuracy(Figure 1, #103, Iyengar discloses calculated the number of errors on an imputation, which can be considered the same as determining accuracy),
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the medical information processing apparatus of Mitsumori to add calculated accuracy, as taught by Iyengar. One of ordinary skill would have been so motivated to provide a means to derive missing medical document data, but accurately to ensure improved patient outcomes, but in this case for a method to determine the best imputation algorithms(Para 0004, Iyengar discloses: “Given the wide range of data imputation algorithms that are available, methods are needed to determine the best ones. The best algorithm is highly dependent on the data set. In addition, multiple criteria can be used to determine the best data imputation algorithms. Accuracy is important as is execution time in healthcare.”).
Claim 2
Mitsumori discloses:
The information processing apparatus according to claim 1, wherein the processor is configured to derive a result, which is obtained by performing determination or classification based on at least one piece of the first information(Para 0057 Mitsumori discloses second determination function), as the second information(Para 0056, Mitsumori discloses second medical data which can be second information).
Claim 3
Mitsumori discloses:
The information processing apparatus according to claim 1, wherein the processor is configured to derive the second information based on a predetermined rule(Para 0057, Mitsumori discloses: “second determination function 155 has determined that medical data satisfying the extraction condition[THE EXTRACTION CONDITION CAN BE A PREDETERMINED RULE]).
Claim 5
Mitsumori discloses:
The information processing apparatus according to claim 1, wherein the processor is configured to derive one piece of the second information(Figure 6, Mitsumori discloses second medical document data which includes second information) based on a plurality of pieces of the first information(Figure 3, Mitsumori discloses multiple finding types[PLURALITY OF FIRST INFORMATION] from which second type information types can be generated).
Claim 6
Mitsumori discloses:
The information processing apparatus according to claim 1, wherein the processor is configured to present the second medical document data by associating the first information, which is used for deriving the second information, with the second information(Figure 6, Mitsumori discloses first information, “Finding 5” being associated with second information (P1 or P2)).
Claim 7
Mitsumori discloses:
The information processing apparatus according to claim 1, wherein the processor is configured to: receive an input of third information related to the patient; and generate the second medical document data in which the second information and the third information are recorded in an identifiable state from each other(Figure 7, Mitsumori discloses what can be considered second medical documents data which includes P3, which can be third information associated with second information (P1 or P2)).
Claim 8
Mitsumori discloses:
The information processing apparatus according to claim 1, wherein the processor is configured to: receive an input of third information related to the patient(Figure 7, P3, Mitsumori discloses third information); generate the second medical document data in which the second information and the third information are recorded(Figure 7, Mitsumori discloses what can be a second medical document data with third information (P3) and second information (P1 or P2)); and present the second medical document data such that the second information and the third information are made to be in an identifiable state(Figure 7, Mitsumori discloses P1, P2 and P3 which are considered to be presented in an identifiable state).
Claim 15
Mitsumori discloses:
The information processing apparatus according to claim 1, wherein the first medical document data and the second medical document data are data obtained by converting documents, which are related to hospital admission and discharge of the patient, into data(Para 0035, Mitsumori discloses: “the display 140 converts data[CONVERSION TO DATA] of each of various kinds of information and various images transferred from the processing circuitry 150 into a display electric signal and outputs the display electric signal.”).
Claim 17
Claim 17 contains similar limitations as claim 1. See claim 1 analysis
Claim 18
Claim 18 contains similar limitations as claim 1. See claim 1 analysis
Response to Arguments
35 U.S.C. 101
(Page 10) Regarding the assertion that the improvement is consistent with the principles of Enfish and Ex Part Desjardins.
Applicant's arguments filed have been fully considered but they are not persuasive. Enfish relied on passing Alice Step 1 and the level of abstract concepts in the claims. In the 101 analysis for the amended claim above, the claims do not meet the requirements to properly rely on Enfish as support. Ex Part Desjardins necessitate that there is an improvement to the machine learning model itself, not an improvement based on altering the inputs and outputs. The claims as presented merely define the input and outputs of a generic machine learning model.
(Page 11) Regarding the assertion under Step 2A, Prong One, the outlined operations cannot be performed in the human mind, particularly the execution of a trained model and quantitative accuracy computation.
Applicant's arguments filed have been fully considered but they are not persuasive. Quantitative accuracy calculations can be performed in the human mind or using pen/paper. The execution of a trained model would amount to mere instructions to apply an exception.
(Page 11-15) Regarding the assertion under Step 2A, Prong Two, the claims are directed to a specific improvement (referring to Ex Part Desjardins and Enfish).
Applicant's arguments filed have been fully considered but they are not persuasive. Enfish relied on passing Alice Step 1 and the level of abstract concepts in the claims. In the 101 analysis for the amended claim above, the claims do not meet the requirements to properly rely on Enfish as support. Ex Part Desjardins necessitate that there is an improvement to the machine learning model itself, not an improvement based on altering the inputs and outputs. The claims as presented merely define the input and outputs of a generic machine learning model and the abstract functions are performed by generic computers.
35 U.S.C. 103
Applicant’s arguments with respect to claim(s) 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Oyez(US20210005297A1) discloses a system for generating a medical report. Some disclosures of this invention are similar to that of this instant pending application.
Futtaim(US20180166162A1) discloses a medical system which classifies information from medical situations. Some disclosures of this invention are similar to that of this instant pending application
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERYL GOPAL PATEL whose telephone number is (703)756-1990. The examiner can normally be reached Monday - Friday 5:30am to 2:30pm PST.
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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