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 .
Status of Claims
This action is in response to the reply to the Response/Election filed 7/14/2025.
Claims 1-6 and 11-13 (Group I) were elected without traverse 7/14/2025.
Claims 7-10 and 14-15 (Group II) were withdrawn without traverse 7/14/2025.
Claims 1-6 and 11-13 are currently pending and have been examined.
Election/Restrictions
Applicant’s election without traverse of Claims 1-6 and 11-13 (Group I) in the reply filed on 7/14/2025 is acknowledged.
Claims 7-10 and 14-15 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group II, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 7/14/2025.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The limitation of “wherein a type of the main feature information which is input to the target prediction model and a weight of each of the main feature information are determined in consideration of the disease information” indicates that a weight is determined (paragraphs 16 and 80) but the specification is silent as to how the weight is determined in relation to the disease information. There are multiple ways to calculate weights of values, therefore the claim is rendered indefinite and clarification is required.
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 1-6 and 11-13 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.
Claims 1-6 and 11-13 are drawn to a method and an apparatus which are statutory categories of invention (Step 1: YES).
Independent claims 1 and 11 recite: predicting an occurrence of a mood episode using a digital phenotype, comprising: acquir[ing] log data associated with a circadian rhythm of a target user from at least one of the target user; extract[ing] predetermined main feature information from the log data; and deduc[ing] prediction information about the occurrence of the mood episode of the target user by inputting the extracted main feature information.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between a user and medical staff, as reflected in the specification, which states that “medical staff terminal 300 refers to a device held by a doctor of the user 1, medical staff, or guardian to acquire prediction information for the occurrence of the mood episode of the user from the predicting apparatus 100 to identify a state of the user. When the occurrence of a specific type of mood episode (for example, a major depressive episode (MDE), a manic episode (ME), or a hypomanic episode (HME)) is predicted, the medical staff terminal generates and transmits guide information including an appropriate action to be taken by the user 1 to the user terminal 220 and/or the wearable device 210.” (see: specification paragraph 40). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “for example, the predicting apparatus 100 provides predetermined questionnaire items with contents associated with a mood episode occurrence to the user terminal 220 of each of the plurality of users at every predetermined examination period and may acquire an input applied to each user terminal 220 in response to the predetermined questionnaire items as answer data.” (see: specification paragraph 71). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “user terminal”, “a wearable device which is attached to or worn on a body of the target user”, and “previously trained artificial intelligence-based prediction model”, “apparatus”, “log collecting unit”, “feature extracting unit”, “analyzing unit” are recited at a high level of generality (e.g., that the acquiring and inputting is performed using generic computer components and a generic artificial intelligence model with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the 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. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
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, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figures 4A-4C, Figure 6 and
Paragraph 39, where “For example, the wearable device 210 may be a smart watch or a smart band which is worn on the wrist of a user to collect biometric information of the user, but is not limited thereto. As another example, the wearable device 210 may broadly refer to a device which is worn on various positions to acquire feature information of the user 1, such as a head-worn device, a strap type device, a garment-type device, or shoe-worn/foot pods device. Further, for example, the wearable device 210 may be Fitbit, Jawbone Up, Nike+ FuelBand, Apple Watch, or Samsung Gear. Further, the wearable device 210 and the user terminal 220 may be interlinked based on the same account information for one user 1. Further, according to an implemented example of the present disclosure, it may be understood that the wearable device 210 is included in the user terminal 220 in a broad sense.”
Paragraph 41, where “For example, the user terminal 220 and/or the medical staff terminal 300 may include a smart phone, a smart pad, and a tablet PC, and terminals of all kinds of wireless communications such as personal communication system (PCS), global system for mobile communication (GSM), personal digital cellular (PDC), personal handy phone system (PHS), personal digital assistant (PDA), international mobile communication (IMT)-2000, code division multiple access (CDMA)-2000, W-code division multiple access (W-CDMA), and wireless broadband internet (Wibro).”
Paragraph 115, “The method for predicting an occurrence of a mood episode using a digital phenotype and the training method of an artificial intelligence-based prediction model for predicting a mood episode relapse using a digital phenotype according to the exemplary embodiment of the present disclosure are implemented in the form of a program instruction to be performed by various computer means to be recorded in a computer readable medium. The computer readable medium may include solely a program command, a data file, and a data structure or a combination thereof. The program instruction recorded in the medium may be specifically designed or constructed for the present disclosure or known to those skilled in the art of a computer software to be used. Examples of the computer readable recording medium include a magnetic media such as a hard disk, a floppy disk, or a magnetic tape, an optical media such as a CD-ROM or a DVD, a magneto-optical media such as a floptical disk, and a hardware device which is specifically configured to store and execute the program command such as a ROM, a RAM, and a flash memory. Examples of the program command include not only a machine language code which is created by a compiler but also a high level language code which may be executed by a computer using an interpreter. The hardware device may operate as one or more software modules in order to perform the operation of the present disclosure, and vice versa.”
Paragraph 37, where “Referring to FIG. 1, a digital health care system according to an exemplary embodiment of the present disclosure may include an apparatus 100 for predicting (hereinafter, referred to as a "predicting apparatus 100") an occurrence of a mood episode using a digital phenotype according to an exemplary embodiment of the present disclosure, a wearable device 210, a user terminal 220, and a medical staff terminal 300.”
Paragraph 90, where “The log collecting unit 140 may acquire log data related to a circadian rhythm of the target user 1 from at least one of the user terminal 220 of the target user 1 and a wearable device 210 which is attached to or worn on a body of the target user 1. Further, the log collecting unit 140 may acquire user information including disease information about the mood disorder type of the target user 1.”
Paragraph 91, where “The feature extracting unit 150 may extract predetermined main feature information from the log data. Specifically, the feature extracting unit 150 may deduce main feature information which is set in advance so as to correspond to the disease information of the target user 1 from the log data.”
Paragraph 92, where “The analysis unit 160 may deduce prediction information about the mood episode occurrence of the target user 1 by inputting the extracted main feature information to a previously trained artificial intelligence-based prediction model. Specifically, the analysis unit 160 may calculate information about the occurrence possibility of at least one of the major depressive episode (MDE), the manic episode (ME), and the hypomanic episode (HME) of the target user 1 within a predetermined analysis period as prediction information.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 2-6, 12-13 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 2, 4, 6 and 12 recite deducing and including healthcare data on the generically recited computing components and generically recited artificial intelligence model as shown in the parent claims above.
Claims 3 and 13 further recite “a target prediction model” and “a plurality of previously trained prediction models” which is recited at a high level of generality (e.g., that the associating and correlating are performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraphs 50 and 21. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 4 further recites “the target predication model” which is recited at a high level of generality (e.g., that the associating and correlating are performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 50. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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.
Claim(s) 1-3, 5-6, 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2022/0139563 A1) in view of Abrams (US 2023/0148923 A1).
CLAIM 1-
Lee teaches:
predicting an occurrence of a mood episode using a digital phenotype, comprising: (Lee teaches log data as a digital phenotype to predict mood episodes (para [0005, 0098, 0116]))
acquiring log data associated with a circadian rhythm of a target user from at least one of a user terminal of the target user and a wearable device which is attached to or worn on a body of the target user; (Lee teaches collecting log data that is associated with a circadian rhythm of a user using a user terminal and a wearable device (para [0029, 0048-0049]))
extracting predetermined main feature information from the log data; (Lee teaches obtaining feature information collected during a predetermined period (i.e., predetermine main feature information based on circadian rhythm data (i.e., log data) (para [0096, 0107-0108]))
Lee teaches deducing prediction information using a trained in advance prediction model (paragraph 94, Figure 5) but does not explicitly teach multiple previously trained artificial intelligence based prediction models, however Abrams teaches:
and deducing prediction information about the occurrence of the mood episode of the target user by inputting the extracted main feature information to a previously trained artificial intelligence-based prediction model (Abrams teaches that the prediction of a brain state episode (i.e., mood episode) is based on AI models that are retrained at regularly intervals (i.e., previously trained) in order to obtain feature information (para [0019, 0089, 0116, 0142]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the mood predicting system of Lee to integrate the psychological brain state predicting system using previously trained AI models of Abrams with the motivation of improving patient diagnosis using data recognition and prediction tactics (see: Abrams, paragraph 9).
CLAIM 2-
Lee in view of Abrams teaches the limitations of claim 1. Regarding claim 2, Lee further teaches:
wherein in the deducing of prediction information, information of an occurrence possibility of at least one of a major depressive episode, a manic episode, and a hypomanic episode of the target user is calculated as the prediction information within a predetermined analysis period (Lee teaches that depressive episodes and manic and hypomanic episodes are calculated within a predetermined analysis period of days/year to predict reoccurrence (para [0115-0116, 0068]))
CLAIM 3-
Lee in view of Abrams teaches the limitations of claim 1. Regarding claim 3, Lee further teaches:
acquiring user information including disease information about a mood disorder type of the target user (Lee teaches obtaining data such as disease history and mood disorder information about the user (para [0082, 0108]))
Lee does not explicitly teach, however Abrams teaches:
wherein in the deducing of prediction information, the prediction information is deduced using a target prediction model which is determined so as to correspond to the disease information, among a plurality of previously trained prediction models (Abrams teaches that the multiple AI models that are retrained (i.e., previously trained) are used to determine brain activity relating to a disease to correspond the data to that specific disease and how closely the prediction matches (i.e., target prediction) (para [0058, 0060, 0067, 0089, 0039]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the mood predicting system of Lee to integrate the psychological brain state predicting system using previously trained AI models of Abrams with the motivation of improving patient diagnosis using data recognition and prediction tactics (see: Abrams, paragraph 9).
CLAIM 5-
Lee in view of Abrams teaches the limitations of claim 3. Regarding claim 5, Abrams further teaches:
wherein a type of the main feature information which is input to the target prediction model and a weight of each of the main feature information are determined in consideration of the disease information (Abrams teaches that inputting the patient data (i.e., main feature information) to determine brain activity relating to a disease to correspond the data to that specific disease and how closely the prediction matches (i.e., target prediction) includes classifying (i.e., weighting) the data and considering the disease features (para [0058, 0060, 0067, 0089, 0039, 0142, 0159]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the mood predicting system of Lee to integrate the psychological brain state predicting system using previously trained AI models of Abrams with the motivation of improving patient diagnosis using data recognition and prediction tactics (see: Abrams, paragraph 9).
CLAIM 6-
Lee in view of Abrams teaches the limitations of claim `. Regarding claim 6, Lee further teaches:
wherein the main feature information includes feature information corresponding to each of a plurality of categories including heart rate information, light exposure information, sleep information, and step information of the target user (Lee teaches that the collected feature information includes light exposure information, step information, sleep information and heart rate information (para [0054]))
CLAIM 11-
Claim 11 is significantly similar to claim 1 and is rejected upon the same prior art as claim 1. Claim 11 further recites “a log collecting unit”, “a feature extracting unit” and “an analyzing unit” that appear to be software units that analyze the data as shown in the specification paragraphs 90-93, and are taught by Lee as a collecting unit, proving unit, and predicting unit respectively in paragraphs 122-127.
CLAIMS 12-13-
Claims 12-13 are significantly similar to claims 2-3 and are rejected upon the same prior art as claims 2-3.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2022/0139563 A1) in view of Abrams (US 2023/0148923 A1) and further in view of Bahn (US 10,386,362 B2).
CLAIM 4-
Lee in view of Abrams teaches the limitations of claim 3. Regarding claim 4, Abrams in view of Lee teach disorder types including spectrum of depressive disorders (i.e., major depressive disorder) and bipolar disorder, but does not explicitly teach, however Bahn teaches:
wherein the mood disorder type includes a major depressive disorder, a bipolar I disorder, and a bipolar II disorder (Bahn teaches diagnosing major depressive disorder, bipolar I and bipolar II disorders based on detecting and comparing patient data (col 2 lines 34-61, col 4 lines 51-67-col 5 lines 111, col 8 lines 1-22))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the mood predicting system with previously trained artificial intelligence models of Lee in view of Abrams to integrate the analyzing of patient data to diagnose bipolar I, bipolar II, and major depressive disorders of Bahn with the motivation of improving patient outcomes by differentially diagnosing psychiatric disorders by analyzing the different pieces of data that indicate each disorder is present (see: Abrams, col 4 lines 1-10).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Calhoun (WO 2019140435 A1) teaches predicting mental disorder changes based on patient data.
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/KIMBERLY A. SASS/Examiner, Art Unit 3686
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