DETAILED ACTION
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 .
Claims 1-8 have been examined.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-6 are drawn to a system which is within the four statutory categories (i.e. machine). Claim 7 is drawn to a method which is within the four statutory categories (i.e. process). Claim 8 is drawn to a non-transitory medium which is within the four statutory categories (i.e. manufacture).
Step 2A, Prong 1:
Claims 1, 7 and 8 recite “receive designation of an extraction condition on observation data indicating an examination content of a patient; and in a case where the designation is received, start a process of extracting a piece of observation data which, among observation data that has been stored in at least one target medical institution after reception of the designation, matches the extraction condition designated and storing the piece of observation data in a database”, and these limitations correspond to an abstract idea of “certain methods of organizing human activity”. This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic processor does not take the claims out of the methods of organizing human interactions grouping. Thus, the claims recite an abstract idea.
The processor is described in the current specification as a generic computing component, such as [0078] recites “Examples of the at least one processor C1 can include a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, and a combination thereof. Examples of the memory C2 can include a flash memory, a hard disk drive (HDD), a solid state drive (SSD), and a combination thereof.”.
Claims 2-6 are ultimately dependent from claim 1 and include all the limitations of claim 1. Therefore, claims 2-6 recite the same abstract idea. Claims 2-6 describe a further limitation regarding the basis for matching extracted data with the extraction condition. These are all just further describing the abstract idea recited in claim 1, without adding significantly more.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “an information processing apparatus”, using a processor to receive a designation of an extraction condition, extracting a piece of observation data, matching the extraction condition with the designation and storing the data in a database,-claims 1, 7, 8, “the observation data is generated by inputting the examination content to a plurality of data items of an electronic medical record; and in the extracting, the at least one processor determines, based on a value of at least one of the plurality of data items which corresponds to the extraction condition in the electronic medical record, whether or not the observation data matches the extraction condition”-claim 2, “in the extracting, the at least one processor applies a conversion rule according to the type of the electronic medical record, the conversion rule converting each of the plurality of data items included in the electronic medical record into a standard data item, and determines whether or not the observation data matches the extraction condition, in view of the plurality of data items after conversion”-claim 3, “the at least one processor carries out a natural language process of inputting, to a language model which has been trained by machine learning, an examination content that is included in the observation data and that is described in a natural language and causing the language model to output data indicating whether or not the examination content matches the extraction condition; and in the extracting, the at least one processor determines whether or not the observation data matches the extraction condition, in view of the data outputted by the language mode”-claim 4, “in the extracting, the at least one processor stores, in the database, the observation data indicating that the patient has the at least one symptom”-claim 5, and “an output control process of causing an output apparatus to output output data that has been generated with use of the observation data stored in the database and that indicates a feature of the at least one symptom; and an updating process of, in a case where new observation data is stored in the database, updating the output data to be outputted by the output apparatus, so that the output data reflects the new observation data”-claim 6.
These additional elements are directed to hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform extracting data and matching condition steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
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.
Claims 1-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kondadadi (US 11,152,084 B2).
Claim 1 recites an information processing apparatus comprising at least one processor, the at least one processor being configured to:
receive designation of an extraction condition on observation data indicating an examination content of a patient (Kondadadi discloses “…processing to extract one or more medical facts ( e.g. , clinical facts ) from the text narrative…” in col. 13, lines 30-39, “…a fact extraction component may also represent various types of relationships between the concepts represented…one type of relationship may be a symptom relationship…”col. 14, lines 17-19, 31-35 and col. 5, line 62 to col. 6, line 43); and
in a case where the designation is received, start a process of extracting a piece of observation data which, among observation data that has been stored in at least one target medical institution after reception of the designation, matches the extraction condition designated and storing the piece of observation data in a database (Kondadadi discloses “In some embodiments, matching of an acronym or abbreviation encountered in a medical report to its proper expanded form in that particular instance may be performed by an acronym/abbreviation expansion model, which may be trained statistically in some embodiments using methods similar to those described above for training the statistical entity detection model and/or the statistical relation model. …” in col. 26, lines 8-14).
Claim 2 recites the information processing apparatus according to claim 1, wherein: the observation data is generated by inputting the examination content to a plurality of data items of an electronic medical record; and in the extracting, the at least one processor determines, based on a value of at least one of the plurality of data items which corresponds to the extraction condition in the electronic medical record, whether or not the observation data matches the extraction condition (Kondadadi; col. 14, lines 17-19, 31-35).
Claim 3 recites the information processing apparatus according to claim 2, wherein in the extracting, the at least one processor applies a conversion rule according to the type of the electronic medical record, the conversion rule converting each of the plurality of data items included in the electronic medical record into a standard data item, and determines whether or not the observation data matches the extraction condition, in view of the plurality of data items after conversion (Kondadadi; col. 16, lines 10-46).
Claim 4 recites the information processing apparatus according to claim 1, wherein: the at least one processor carries out a natural language process of inputting, to a language model which has been trained by machine learning, an examination content that is included in the observation data and that is described in a natural language and causing the language model to output data indicating whether or not the examination content matches the extraction condition; and in the extracting, the at least one processor determines whether or not the observation data matches the extraction condition, in view of the data outputted by the language model (Kondadadi; col. 6, lines 44-49).
Claim 5 recites the information processing apparatus according to claim 1, wherein: the extraction condition indicates at least one symptom; and in the extracting, the at least one processor stores, in the database, the observation data indicating that the patient has the at least one symptom (Kondadadi; col. 14, lines 31-35 and col. 16, lines 10-46).
Claim 6 recites the information processing apparatus according to claim 5, wherein the at least one processor carries out: an output control process of causing an output apparatus to output output data that has been generated with use of the observation data stored in the database and that indicates a feature of the at least one symptom; and an updating process of, in a case where new observation data is stored in the database, updating the output data to be outputted by the output apparatus, so that the output data reflects the new observation data (Kondadadi; col. 14, lines 31-35 and col. 16, lines 10-46).
Claim 7 recites a data extraction method comprising:
a reception process of, by at least one processor, receiving designation of an extraction condition on observation data indicating an examination content of a patient (Kondadadi discloses “…processing to extract one or more medical facts ( e.g. , clinical facts ) from the text narrative…” in col. 13, lines 30-39, “…a fact extraction component may also represent various types of relationships between the concepts represented…one type of relationship may be a symptom relationship…”col. 14, lines 17-19, 31-35 and col. 5, line 62 to col. 6, line 43),
the at least one processor, in a case where the reception process is carried out, starting a process of extracting a piece of observation data which, among observation data that has been stored in at least one target medical institution after reception of the designation, matches the extraction condition designated and storing the piece of observation data in a database (Kondadadi discloses “In some embodiments, matching of an acronym or abbreviation encountered in a medical report to its proper expanded form in that particular instance may be performed by an acronym/abbreviation expansion model, which may be trained statistically in some embodiments using methods similar to those described above for training the statistical entity detection model and/or the statistical relation model. …” in col. 26, lines 8-14 and col. 5, line 62 to col. 6, line 43).
Claim 8 recites a computer-readable non-transitory storage medium storing a data extraction program that causes a computer to:
carry out a reception process of receiving designation of an extraction condition on observation data indicating an examination content of a patient (Kondadadi discloses “…processing to extract one or more medical facts ( e.g. , clinical facts ) from the text narrative…” in col. 13, lines 30-39, “…a fact extraction component may also represent various types of relationships between the concepts represented…one type of relationship may be a symptom relationship…”col. 14, lines 17-19, 31-35 and col. 5, line 62 to col. 6, line 43); and
in a case where the designation is received, start a process of extracting a piece of observation data which, among observation data that has been stored in at least one target medical institution after reception of the designation, matches the extraction condition designated and storing the piece of observation data in a database (Kondadadi discloses “In some embodiments, matching of an acronym or abbreviation encountered in a medical report to its proper expanded form in that particular instance may be performed by an acronym/abbreviation expansion model, which may be trained statistically in some embodiments using methods similar to those described above for training the statistical entity detection model and/or the statistical relation model. …” in col. 26, lines 8-14).
Conclusion
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687