DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the filed on 11/4/2022.
Claims 1-10 currently pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statement filed on 11/4/2022 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Priority
Applicant’s claim for the benefit of prior-filed applications (Japanese application JP2021-185031, filed 11/12/2021) under 35 U.S.C. 110(e) or under 35 U.S.C. 120, 121, or 365(c), or under 35 U.S.C. 119(a)-(d) or (f) is acknowledged.
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.
Human Interactions Organized
Applicant discloses (Applicant’s Specification, [0008]) the need to take into consideration the influence of confounding factors (attributes, such as gender and age, and past intervention results). So a need exists to organize these human interactions by/through predicting an effect of intervention on a person using the steps of “managing first modes, executing a prediction process, calculating output values, calculating features, calculating prediction values, mapping output values,” etc. Applicant’s system is therefore a certain method of organizing the human activities as described and disclosed by Applicant.
Rejection
Claim(s) 1-10 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 1 and 6 is/are directed to the abstract idea of “predicting an effect of intervention on a person,” etc. (Applicant’s Specification, Abstract, paragraph(s) [0002]), etc., as explained in detail below, and thus grouped as a certain method of organizing human interactions. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea. Accordingly, claims 1-10 recite an abstract idea.
Claim(s) 1 and 6 is/are directed to the abstract idea of “predicting an effect of intervention on a person,” etc.
Step 2A Prong 1 – The Judicial Exception
The claim(s) recite(s) in part, system for performing the steps of “managing first modes, executing a prediction process, calculating output values, calculating features, calculating prediction values, mapping output values,” etc., that is “predicting an effect of intervention on a person,” etc. which is a method of managing personal behavior or relationships or interactions between people (social activities, teaching, following rules, instructions) and thus grouped as a certain method of organizing human interactions. Accordingly, claims 1-10 recite an abstract idea.
Step 2A Prong 2 – Integration of the Judicial Exception into a Practical Application
This judicial exception is not integrated into a practical application because the generically recited additional computer elements (i.e. computer, CPU, main storage device, auxiliary storage device, output device, input device, network adapter, information terminal, external recording device (Applicant’s Specification [0026]-[0039]), etc.) to perform steps of “managing first modes, executing a prediction process, calculating output values, calculating features, calculating prediction values, mapping output values,” etc. do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and this is nothing more than an attempt to generally link the product of nature to a particular technological environment. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limit on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Insignificant extra-solution activity
Claim(s) 1-10 recites storing data steps, retrieving data steps, providing data steps, output steps (Bilski v. Kappos, 561 U.S. 593, 610-12 (2010), Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Can., 771 F.Supp.2d 1054, 1066 (E.D. Mo. 2011), aff’d, 687 F.3d at 1266), and/or transmitting data step (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014), Apple, Inc. v. Ameranth, Inc., 842 F.3d 1299, 1241-42 (Fed. Cir. 2016)) that is/are insignificant extra-solution activity. Extra-solution activity limitations are insufficient to transform judicially excepted subject matter into a patent-eligible application (MPEP §2106.05(g)).
Step 2B – Search for an Inventive Concept/Significantly More
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements (i.e. computer, CPU, main storage device, auxiliary storage device, output device, input device, network adapter, information terminal, external recording device, etc.) are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept (Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)). Accordingly, the claims are not patent eligible.
Individually and in Combination
The additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The additional elements amount to no more than generic computer components that serve to merely link the abstract idea to a particular technological environment (i.e. computer, CPU, main storage device, auxiliary storage device, output device, input device, network adapter, information terminal, external recording device, etc.). At paragraph(s) [0026]-[0039], Applicant’s specification describes generic computer hardware for implementing the above described functions including “computer, CPU, main storage device, auxiliary storage device, output device, input device, network adapter, information terminal, external recording device,” etc. to perform the functions of “managing first modes, executing a prediction process, calculating output values, calculating features, calculating prediction values, mapping output values,” etc. The recited “computer, CPU, main storage device, auxiliary storage device, output device, input device, network adapter, information terminal, external recording device,” etc. does/do not add meaningful limitations to the idea of beyond generally linking the system to a particular technological environment, that is, implementation via computers. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). 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 improves any other technology, or improves a technical field, or provides a technical improvement to a technical problem. Their collective functions merely provide generic computer implementation. Therefore, claims 1-10 do not amount to significantly more than the underlying abstract idea of “an idea of itself” (Alice).
Dependent Claims
Dependent claim(s) 2-5 and 7-10 include(s) all the limitations of the parent claims and are directed to the same abstract idea as discussed above and incorporated herein.
Although dependent claims 2-5 and 7-10 add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. Dependent claims 2-5 and 7-10 merely describe physical structures to implement the abstract idea. These information and physical characteristics do not change the fundamental analogy to the abstract idea grouping of certain method of organizing human interactions, and when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as independent claim(s) 1 and 6.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-10 are rejected under 35 U.S.C. 102(a)(1)(2) as being anticipated by Harinen (US 2023/0131677).
CLAIM
As per claim , Harinen disclose(s):
a computer system that predicts an effect of a plurality of interventions on a person, the computer system comprising at least one computer including a processor and a storage device connected to the processor (Harinen, Figure 1, Figure 2, Figure 3),
wherein the computer system manages a first model that calculates an output value, using time-series data including a value related to an intervention carried out on a person, a second model generated by machine learning, the second model calculating a feature by mapping an output value from the first model onto a feature space, and a third model that outputs a predicted value of an effect of an intervention on the person, based on the feature (Harinen, Figure 1, Figure 2, Figure 3),
wherein the time-series data includes a plurality of data strings including a time at which the intervention is carried out on the person, a plurality of factors indicating a state of the person, and values indicating a type and a degree of the intervention carried out on the person (Harinen, Figure 1, Figure 2, Figure 3),
wherein the processor executes a prediction process (Harinen, Figure 1, Figure 2, Figure 3) including:
calculating the output value by inputting the data string to the first model; calculating the feature by inputting the output value to the second model (Harinen, Figure 1, Figure 2, Figure 3); and
calculating a predicted value of an effect of the intervention carried out continuously, the intervention corresponding to the time-series data, by inputting the feature to the third model (Harinen, Figure 1, Figure 2, Figure 3), and
wherein the second model maps an output value from the first model onto the feature space so that a difference in distribution of a plurality of data strings used in the machine learning reduces in the feature space (Harinen, Figure 1, Figure 2, Figure 3).
CLAIM 2
As per claim 2, Harinen disclose(s) the system/method/computer readable medium of claim and further disclose(s) the limitations of:
wherein the computer system manages a fourth model that identifies a type of the intervention carried out on the person, from the feature, and a loss function defined by a predicted type of the intervention outputted by the fourth model, a type of the intervention included in learning data, a predicted value of an effect of the intervention, and an effect value of the intervention included in the learning data, and wherein the processor executes the machine learning including: receiving the learning data including a plurality of data strings including identification information on the person, a time at which the intervention is carried out on the person, values of the plurality of factors of the person, a type and a degree of the intervention the person has undergone, and an effect value of the intervention; inputting the data string to the first model and inputting the output value outputted from the first model, to the second model; calculating a predicted value of an effect of the intervention by inputting the feature outputted from the second model, to the third model; calculating a predicted type of the intervention by inputting the feature outputted from the second model, to the fourth model; calculating a value of the loss function, using a type of the intervention and an effect value of the intervention in each of the plurality of data strings, and a predicted type of the intervention and a predicted value of an effect of the intervention that are calculated from each of the plurality of data strings; and updating the second model, the third model, and the fourth model, using the value of the loss function (Harinen, Figure 1, Figure 2, Figure 3).
CLAIM 3
As per claim 3, Harinen disclose(s) the system/method/computer readable medium of claim 2 and further disclose(s) the limitations of:
wherein the loss function is defined by a first loss function that evaluates a sum of errors between an effect value of the intervention, the effect value being included in the data string, and a predicted value of an effect of the intervention, the predicted value being calculated from the data string, and by a second loss function that evaluates a sum of errors between a type of the intervention, the type being included in the data string, and a predicted type of the intervention, the predicted type being calculated from the data string (Harinen, Figure 1, Figure 2, Figure 3).
CLAIM 4
As per claim 4, Harinen disclose(s) the system/method/computer readable medium of claim and further disclose(s) the limitations of:
wherein the processor presents a first user interface for adjusting a type and a degree of the intervention in at least one data string included in the time-series data and a timing of carrying out the intervention, and wherein the processor executes the prediction process, using the time-series data including a data string inputted through the first user interface (Harinen, Figure 1, Figure 2, Figure 3).
CLAIM 5
As per claim 5, Harinen disclose(s) the system/method/computer readable medium of claim and further disclose(s) the limitations of:
wherein the processor presents a second user interface for displaying a predicted value of an effect of the intervention, the predicted value being calculated from each of the plurality of data strings, wherein the processor receives corrective content of the predicted value of the effect of the intervention through the second user interface, and wherein the processor executes the prediction process, using the time-series data including a data string reflecting the corrective content of the predicted value of the effect of the intervention, the corrective content being inputted through the second user interface (Harinen, Figure 1, Figure 2, Figure 3).
CLAIMS 6-10
As per claims 6-10, claims 6-10 are directed to a method. Claims 6-10 recite the same or similar limitations as those addressed above for claims 1-5. Claims 6-10 are therefore rejected for the same reasons set forth above for claims 1-5.
Prior Art
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO-892 and include:
Kaniwa et al. (US 2017/0351972) disclose a learning model difference providing method that causes a computer to execute a process which includes: calculating a mismatch degree between prediction data about arbitrary data included in a plurality of pieces of data that are input by using an application program, the prediction data being obtained by the plurality of pieces of data and a learning model in accordance with a purpose of use of the application program.
Tateno et al. (US 2023/0050451) disclose a processing unit includes a contribution-degree calculation unit configured to calculate contribution degrees that indicate respective degrees by which a plurality of attribute items included as predetermined attributes of one target contribute to a predicted intervention effect, the calculating being performed on a basis of an estimation model for estimating the predicted intervention effect from the predetermined attributes of the one target.
Stimpson et al. 2014 (Reference U) disclose a paper with the goals of: 1) determine the impact of process-level information on machine learning prediction results and 2) establish the effect of type of machine learning algorithm used on prediction results. Data were collected from a university level course in human factors engineering (n D 35), which included both traditional classroom assessment and computer-based assessment methods.
Comendador et al. 2016 (Reference V) disclose a paper that examined the students’ history of accessing the university Learning Management System (LMS) data. Classification techniques are used to build an educational model based on Knowledge Discovery in Databases (KDD) to predict learner’s behavior. It identified the most valuable influencer for learning outcomes of the learners; it generated prediction models using the J48 decision tree algorithm and Multiple linear regression; and it determined how likely is a Distance Education (DE) learners to get a mark of “Passed” in a certain course which may offer vital information to the teachers and university administrators for program planning and learner support strategies.
Conclusion
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/C. P. C./
Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683
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