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 .
DETAILED ACTION
The present application is being examined under the pre-AIA first to invent provisions.
Status of Claims
This action is in reply to the application filed on March 18, 2024.
2. Claim(s) 1-16 are currently pending and 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 1-16 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1 and 13-16 are directed to an abstract idea consisting of a system and method for generating personalized health action recommendations by training a learning model on historical health state and behavioral data, and then applying the trained model to an individual's history to output suggested actions aimed at improving their health.
Independent Claim 1 recites “acquiring history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state, and success / failure information indicating whether or not the action contributed to the variation in the health state of the target person; and training a model based on the history information and the success /failure information, wherein the model is configured to output information regarding a recommended action recommended to improve the health state of the target person upon receiving an input of the history information indicating the history of the action and the health state of the target person.”
Independent Claim 13 recites “acquiring history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state, and success / failure information indicating whether or not the action contributed to the variation in the health state of the target person; and training a model based on the history information and the success /failure information, wherein the model is configured to output information regarding a recommended action recommended to improve the health state of the target person upon receiving an input of the history information indicating the history of the action and the health state of the target person.”
Independent Claim 14 recites “acquiring history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state; determining a recommended action to be recommended to the target person based on the history information and a recommendation model; and outputting information regarding the recommended action, wherein the recommendation model is a model which learned a relation between each health state of subjects and each recommended action to be recommended to improve the health state of the subjects, based on history information indicating histories of health states of the subjects and actions of the subjects which contribute to variation in the health states.”
Independent Claim 15 recites “acquiring history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state, and success / failure information indicating whether or not the action contributed to the variation in the health state of the target person; and training a model based on the history information and the success /failure information, wherein the model is configured to output information regarding a recommended action recommended to improve the health state of the target person upon receiving an input of the history information indicating the history of the action and the health state of the target person.”
Independent Claim 16 recites “acquiring history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state; determining a recommended action to be recommended to the target person based on the history information and a recommendation model; and outputting information regarding the recommended action, wherein the recommendation model is a model which learned a relation between each health state of subjects and each recommended action to be recommended to improve the health state of the subjects, based on history information indicating histories of health states of the subjects and actions of the subjects which contribute to variation in the health states.”
The limitations of Claims 1 and 13-16, as drafted, under its broadest reasonable interpretation, covers the performance of a “Mental Process” which are concepts performed in the human mind (including an observation, evaluation, judgment, opinion), but for the recitation of generic computer components. That is, other than reciting, “computer” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “computer” language, “acquiring” in the context of this claim encompasses the user manually retrieving history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state, and success / failure information indicating whether or not the action contributed to the variation in the health state of the target person. Similarly, the outputting, information regarding a recommended action recommended to improve the health state of the target person upon receiving an input of the history information indicating the history of the action and the health state of the target person, covers performance of the limitation in the mind, but for the recitation of generic computer components. 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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “computer system, memory” to perform all of the “obtaining, transforming, parsing, determining, transforming, selecting and storing” steps. The “computer system, memory” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1 and 13-16 do not disclose additional elements other than what’s presented in the preambles (i.e., computer and/or storage medium). Looking to the specification, these components are described at a high level of generality (¶ 89 and 103; The terminal device 8 is a terminal having an input function, a display function, and a communication function, and functions as the input device 4 and the output device 5 shown in FIG. 1. Examples of the terminal device 8 include a personal computer, a tablet-type terminal, and a PDA (Personal Digital Assistant). The terminal device 8 transmits a biometric signal outputted by the sensor 6 or an input signal based on a user input to the action recommendation device 2A). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-12). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Mental Processes,” 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 the independent claims.
Claims 1-16 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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.
Claims 1-12 are 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 terms “acquisition means, learning means, history information acquisition means, recommended action determination means, output means, basis information generation means” in claims 1-12 are relative terms which renders the claim indefinite. The terms “acquisition means, learning means, history information acquisition means, recommended action determination means, output means, basis information generation means” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For example, paragraph 6 of Applicant specification states: One aspect of the learning device is a learning device including: an acquisition means configured to acquire history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state, and success / failure information indicating whether or not the action contributed to the variation in the health state of the target person; and a learning means configured to train a model based on the history information and the success / failure information, wherein the model is configured to output information regarding a recommended action recommended to improve the health state of the target person upon receiving an input of the history information indicating the history of the action and the health state of the target person. Appropriate clarification and corrections are required.
Claim Rejections - 35 USC § 102
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 –
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
Claims 1-16 are rejected under 35 U.S.C. 102(b) as being anticipated by Pub. No.: US 20200303047 A1 to Bostic et al.
As per Claim 1, Bostic et al. teaches a learning device comprising:
-- an acquisition means configured to acquire history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state, and success / failure information indicating whether or not the action contributed to the variation in the health state of the target person (see Bostic et al. paragraphs 50, 161 and 208; The system can include a machine learning device in communication with the healthcare database and configured to receive the demographic records, the diagnosis records, the prescription records, and the testing records from the healthcare database. The machine learning device is configured to train an artificial intelligence based on the demographic records, the diagnosis records, the prescription records, and the testing records.); and
-- a learning means configured to train a model based on the history information and the success / failure information, wherein the model is configured to output information regarding a recommended action recommended to improve the health state of the target person upon receiving an input of the history information indicating the history of the action and the health state of the target person (see Bostic et al. paragraphs 50, 161 and 208; The system can include a machine learning device in communication with the healthcare database and configured to receive the demographic records, the diagnosis records, the prescription records, and the testing records from the healthcare database. The machine learning device is configured to train an artificial intelligence based on the demographic records, the diagnosis records, the prescription records, and the testing records.).
As per Claim 2, Bostic et al. teaches the learning device according to claim 1, wherein the history information is information that indicates the action and the health state observed after the action alternately in time series (see Bostic et al. paragraphs 50, 161 and 208; the diagnosis records, the prescription records, and the testing records).
As per Claim 3, Bostic et al. teaches the learning device according to claim 1 or 2, wherein the history information includes, as a record of the action, information regarding a type of the action and a degree of the action series (see Bostic et al. paragraphs 50, 161 and 208; the prescription records).
As per Claim 4, Bostic et al. teaches the learning device according to claim 1 to 3, wherein the history information includes, as a record of the health state, an indicator related to the health state used for calculation of a benchmark indicator used as a criterion for determining whether or not the history is the success example series (see Bostic et al. paragraphs 50, 161 and 208; the diagnosis records, the prescription records, and the testing records).
As per Claim 5, Bostic et al. teaches an action recommendation device comprising:
-- a history information acquisition means configured to acquire history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state; a recommended action determination means configured to determine a recommended action to be recommended to the target person based on the history information and a recommendation model; and an output means configured to output information regarding the recommended action, wherein the recommendation model is a model which learned a relation between each health state of subjects and each recommended action to be recommended to improve each health state of the subjects, based on history information indicating histories of health states of the subjects and actions of the subjects which contribute to variation in the health states (see Bostic et al. paragraphs 50, 161 and 208; The system can include a machine learning device in communication with the healthcare database and configured to receive the demographic records, the diagnosis records, the prescription records, and the testing records from the healthcare database. The machine learning device is configured to train an artificial intelligence based on the demographic records, the diagnosis records, the prescription records, and the testing records.).
As per Claim 6, Bostic et al. teaches the action recommendation device according to claim 5, wherein the recommended action determination means is configured to generate recommended action promotion information for notifying the target person of the recommended action, and wherein the output means is configured to further output the recommended action promotion information (see Bostic et al. paragraphs 50, 161 and 208; The pharmacological tracking platform 100 may use the obtained data to: make recommendations relating to the types of lab tests patients should undertake before beginning a potential prescription; determine whether a patient may be misusing a controlled medication (e.g., an opiate, a benzodiazepine, or amphetamine); determine whether a physician is overprescribing a controlled medication; determine whether a physician or clinic is over-ordering or under-ordering lab tests for their patients; and/or assess the quality of a testing lab. The pharmacological tracking platform 100 may perform additional or alternative tasks without departing from the scope of the disclosure.).
As per Claim 7, Bostic et al. teaches the action recommendation device according to claim 5 or 6, further comprising a basis information generation means configured to generate basis information regarding a basis of determining the recommended action, wherein the output means is configured to output the basis information (see Bostic et al. paragraphs 50, 161 and 208).
As per Claim 8, Bostic et al. teaches the action recommendation device according to any one of claims 5 to 7, further comprising a determination means configured to determine whether or not a timing of recommending an action to the target person comes up, wherein upon determining that the timing has come up, the output means is configured to output the information regarding the recommended action (see Bostic et al. paragraphs 50, 161 and 208).
As per Claim 9, Bostic et al. teaches the action recommendation device according to any one of claims 5 to 8, further comprising a target person data acquisition means configured to acquire target person data, which is data regarding the target person, wherein the history information acquisition means is configured to generate the history information based on the target person data (see Bostic et al. paragraphs 50, 161 and 208).
As per Claim 10, Bostic et al. teaches the action recommendation device according to claim 9, wherein the target person data includes a signal outputted by a sensor which measures the target person (see Bostic et al. paragraphs 50, 161 and 208).
As per Claim 11, Bostic et al. teaches the action recommendation device according to claim 9 or 10, wherein the target person data acquisition means is configured to acquire the target person data from a terminal device used by the target person, wherein the output control means is configured to transmit the information regarding the recommended action to the terminal device (see Bostic et al. paragraphs 50, 161 and 208).
As per Claim 12, Bostic et al. teaches the action recommendation device according to any one of claims 9 to 11, wherein the history information acquisition means is configured to generate the history information based on the diagnosis data of a medical checkup undergone by the target person (see Bostic et al. paragraphs 50, 161 and 208).
As per Claim 13, Bostic et al. teaches a learning method executed by a computer, the learning method comprising:
-- acquiring history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state, and success / failure information indicating whether or not the action contributed to the variation in the health state of the target person (see Bostic et al. paragraphs 50, 161 and 208; The system can include a machine learning device in communication with the healthcare database and configured to receive the demographic records, the diagnosis records, the prescription records, and the testing records from the healthcare database. The machine learning device is configured to train an artificial intelligence based on the demographic records, the diagnosis records, the prescription records, and the testing records.); and
-- training a model based on the history information and the success /failure information, wherein the model is configured to output information regarding a recommended action recommended to improve the health state of the target person upon receiving an input of the history information indicating the history of the action and the health state of the target person (see Bostic et al. paragraphs 50, 161 and 208; The system can include a machine learning device in communication with the healthcare database and configured to receive the demographic records, the diagnosis records, the prescription records, and the testing records from the healthcare database. The machine learning device is configured to train an artificial intelligence based on the demographic records, the diagnosis records, the prescription records, and the testing records.).
As per Claim 14, Bostic et al. teaches an action recommendation method executed by a computer, the recommendation method including:
-- acquiring history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state; determining a recommended action to be recommended to the target person based on the history information and a recommendation model; and outputting information regarding the recommended action, wherein the recommendation model is a model which learned a relation between each health state of subjects and each recommended action to be recommended to improve the health state of the subjects, based on history information indicating histories of health states of the subjects and actions of the subjects which contribute to variation in the health states (see Bostic et al. paragraphs 50, 161 and 208; The system can include a machine learning device in communication with the healthcare database and configured to receive the demographic records, the diagnosis records, the prescription records, and the testing records from the healthcare database. The machine learning device is configured to train an artificial intelligence based on the demographic records, the diagnosis records, the prescription records, and the testing records.).
As per Claim 15, Bostic et al. teaches a storage medium storing a program executed by a computer, the program causing the computer to:
-- acquire history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state, and success / failure information indicating whether or not the action contributed to the variation in the health state of the target person; and train a model based on the history information and the success / failure information, wherein the model is configured to output information regarding a recommended action recommended to improve the health state of the target person upon receiving an input of the history information indicating the history of the action and the health state of the target person (see Bostic et al. paragraphs 50, 161 and 208; The system can include a machine learning device in communication with the healthcare database and configured to receive the demographic records, the diagnosis records, the prescription records, and the testing records from the healthcare database. The machine learning device is configured to train an artificial intelligence based on the demographic records, the diagnosis records, the prescription records, and the testing records.).
As per Claim 16, Bostic et al. teaches a storage medium storing a program executed by a computer, the program causing the computer to:
-- acquiring history information indicating a history of a health state of a target person and an action of the target person contributing to variation in the health state; determining a recommended action to be recommended to the target person based on the history information and a recommendation model; and outputting information regarding the recommended action, wherein the recommendation model is a model which learned a relation between each health state of subjects and each recommended action to be recommended to improve the health state of the subjects, based on history information indicating histories of health states of the subjects and actions of the subjects which contribute to variation in the health states (see Bostic et al. paragraphs 50, 161 and 208; The system can include a machine learning device in communication with the healthcare database and configured to receive the demographic records, the diagnosis records, the prescription records, and the testing records from the healthcare database. The machine learning device is configured to train an artificial intelligence based on the demographic records, the diagnosis records, the prescription records, and the testing records.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pub. No.: US 20210005324 A1 to Bostic et al.; A computerized method for healthcare data management generally includes receiving a symptom report from an individual, and receiving, at a healthcare data system computing device, continuous health information including data related to an individual's health state and data related to a health state of a population of patients; calculating, at the healthcare data system computing device, a risk score, wherein the risk score is based on at least the individual's symptom report, on data related to an individual's health state, and on the health state of a population of patients, wherein the population of patients share an attribute with the individual; and sending, from the healthcare data system computing device, a return-to-work recommendation to an entity based at least in part on the calculated risk score.
Pub. No.: US 20210202103 A1; Systems and methods are provided for simulating a patient health state by determining one or more relationships between patient data and historical data, creating enriched data elements based on the determined relationships, and using a machine learning module to compute a current health state for a patient and to simulate a future health state of the patient.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683