DETAILED ACTION
The present office action represents a nonfinal action on the merits.
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 .
Priority
This application claims the priority date of foreign application EP21179638.8 dated June 15, 2021 and 371 of PCT/EP2022/066202 dated June 14, 2022.
Status of Claims
Claim 4 is amended and claims 1-26 are pending.
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 25-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because there are no structural elements in the claims.
Claims 25-26 claim software, a “computer related product”, however, fail to claim the necessary hardware. Pursuant to MPEP 2106.03, a machine is a "concrete thing, consisting of parts, or of certain devices and combination of devices." Digitech, 758 F.3d at 1348-49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)). This category "includes every mechanical device or combination of mechanical powers and devices to perform some function and produce a certain effect or result." Nuijten, 500 F.3d at 1355, 84 USPQ2d at 1501 (quoting Corning v. Burden, 56 U.S. 252, 267, 14 L. Ed. 683, 690 (1854)). Here, the claims recite “computer related product” but fail to disclose any structural element, no computer or device is claimed, and therefore, can be construed as software per se, which is non-statutory.
Claims 1-26 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 1-13 are drawn to a method for monitoring young children, which is within the four statutory categories (i.e., process). Claims 14-24, are drawn to a system for monitoring young children, which is within the four statutory categories (i.e., machine). Claims 25-26 are drawn to a computer related product, which is not within the four statutory categories (i.e., software per se); the claims may fall within a statutory category once amended by Applicant.
Claims 1-13 recite method for monitoring young children comprising:
providing signals and/or converted sensor data from a plurality of sensors ;
managing a health status by a management component configured to receive the sensor data on the basis of a model.
Claims 14-24 recite system for monitoring young children comprising:
a plurality of sensors providing signals that are configured to be converted into sensor data;
a management component configured for receiving the sensor data and for managing a health status and or disease progression on the basis of a model.
Claims 25-26 recite a computer related product for carrying out the method of claim 1.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and mathematical concepts. The underlined limitations are not part of the identified abstract idea (the method of organizing human activity and mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below. If a claim limitation, under its broadest reasonable interpretation, is managing personal behavior or interactions between people and mathematical concepts but for the recitation of generic computer components, then it fails within the “method of organizing human activity” or “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Dependent claims 2-13 and 15-26 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
The dependent claims recite additional limitations but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 14.
The additional elements from claims 1 and 14 include:
a plurality of sensors (apply it, MPEP 2106.05(f)).
The additional elements from claim 14 include:
system (apply it, MPEP 2106.05(f)).
Furthermore, claims 1-26 are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of “a plurality of sensors”, and “system”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see, Specification Pages 6-7. (See MPEP 2106.05(f)).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) 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 Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., Specification Pages 6-7 disclose that the additional elements (i.e., a plurality of sensors and system) comprise a plurality of different types of generic computing systems that are configured to perform generic computer that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., providing information on medication).
Dependent claims 2-13 and 15-26 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than those found in the independent claims.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. 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 health management within the first 1000 days of life or improves any other technology, and their collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, claims 1-26 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 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.
Claims 1-11, 14-22, and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (U.S. Pub. No. 2005/0088296 A1) in view of Peters (U.S. Pub. No. 2022/0262462 A1).
Regarding claim 1, Lee discloses method for monitoring young children comprising (Paragraph [0015] discusses baby health monitoring system and method.):
providing signals and/or converted sensor data from a plurality of sensors (Paragraphs [0022]-[0025] and [0068]-[0069] discuss baby monitoring device with sensors and data from sensors.);
managing a health status by a management component configured to receive the sensor data (Paragraphs [0060] discuss baby’s sensor data the condition of the baby can be read directly from the baby monitoring device and thus a parent or doctor may be immediately informed if any abnormal condition is detected, when an abnormal condition of a baby is detected, this can be transmitted online to a computer at hospital.).
Lee does not explicitly disclose:
managing a health status on the basis of a model.
Peters teaches:
managing a health status on the basis of a model (Paragraphs [0003] and [0006] discuss classifying a medical condition of a mammal (e.g., a human) using predictive models.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, managing a health status on the basis of a model, as taught by Peters, in order to improve efficiency of the prediction. (Peters Paragraph [0003]).
Regarding claim 2, Lee discloses wherein the method comprises monitoring young children between their conception and the subsequent 1,000 days (Paragraphs [0007], [0053] discuss continuously monitoring the baby to assist in the raising of children, especially a baby that is less than 3 calendar years old.).
Lee does not explicitly disclose:
monitoring young children from conception.
Peters teaches:
monitoring young children from conception (Paragraphs [0148]-[0149] discuss early detection of prenatal disorders, diagnosis, and monitoring.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, monitoring young children from conception, as taught by Peters, in order to improve efficiency of the prediction. (Peters Paragraph [0003]).
Regarding claims 3 and 15, Lee does not explicitly disclose wherein the management component comprises a prediction component for predicting the health status on the basis of a model.
Peters teaches:
wherein the management component comprises a prediction component for predicting the health status on the basis of a model (Paragraphs [0003] and [0006] discuss classifying a medical condition of a mammal (e.g., a human) using predictive models.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, wherein the management component comprises a prediction component for predicting the health status on the basis of a model, as taught by Peters, in order to improve efficiency of the prediction. (Peters Paragraph [0003]).
Regarding claims 4 and 16, Lee discloses wherein the management component is further configured to compute at least one of the following data:
health data from a health data base (Paragraphs [0015]-[0017] and [0079] discuss facilitation determination of health of a baby monitoring sensor data stored in a database.);
individual data from a storage (Paragraphs [0065]-[0067] and [0087] discuss infection pattern obtained from the subject baby may be compared with a stored database of infection pattern to find its match and thus identify the type of infection.).
Regarding claims 5 and 17, Lee discloses wherein the plurality of sensors is configured to be attached to a young child and/or its mother (Paragraph [0007] discusses a device that is worn by a baby includes a temperature sensor, and optionally, a variety of other sensors.).
Regarding claim 6, Lee discloses wherein the method comprises receiving sensor data by a computing component (Paragraph [0060] discusses the computer at home or computer at hospital can also communicate with a cell phone and the doctor who has received information about the baby through the computer at hospital can inform parents about more exact diagnosis and rapid treatment if need be.).
Regarding claims 7 and 19, Lee discloses wherein the management component further comprises a sensor data storage that is configured for storing the sensor data from the sensors (Paragraphs [0005]-[0006], [0019], and [0024]-[0025] discuss a device with sensors that continuously records the temperature of a baby and its surrounding environmental conditions, and stores this information, which is used to determine the reason for a change in a baby's temperature, would be advantageous in helping a doctor understand the etiology of a baby's illness, leading to greater possibility of correct diagnosis and care, provides monitored and stored data in a server of a baby to assist the parent and doctor in providing advantageous health care for babies.).
Regarding claims 8 and 18, Lee discloses wherein the management component further comprises a processing component that is configured for processing sensor data received from the sensors (Paragraphs [0009] discuss baby health monitoring and a skin temperature sensor connected to a microprocessor for mathematically converting the sensed temperature to corrected skin temperature.).
Regarding claims 9 and 20, Lee discloses wherein the management component further comprises a computing component that is configured for computing the sensor data (Paragraphs [0002] and [0007] discuss a device that is worn by a baby, the device includes a temperature sensor, and optionally, a variety of other sensors, a network system, which connects the baby monitoring device with computers at home and hospital for detecting infection early in a microorganismic infection cycle.).
Lee does not explicitly disclose:
wherein the management component further comprises applying the model.
Peters teaches:
wherein the management component further comprises applying the model (Paragraphs [0003], [0006], [0237] discuss classifying a medical condition of a mammal (e.g., a human) using predictive models.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, wherein the management component further comprises applying the model, as taught by Peters, in order to improve efficiency of the prediction. (Peters Paragraph [0003]).
Regarding claims 10 and 21, Lee discloses wherein the management component further comprises a computing component that is configured for computing the sensor data (Paragraphs [0002] and [0007] discuss a device that is worn by a baby, the device includes a temperature sensor, and optionally, a variety of other sensors, a network system, which connects the baby monitoring device with computers at home and hospital for detecting infection early in a microorganismic infection cycle.).
Lee does not explicitly disclose:
a computer component for establishing the model.
Peters teaches:
a computer component for establishing the model (Paragraphs [0003]-[0004], [0006], [0100]-[0101], and [0237] discuss classifying a medical condition of a mammal (e.g., a human) using predictive models, some machine-learning techniques determine a model for performing the desired task.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, a computer component for establishing the model, as taught by Peters, in order to improve efficiency of the prediction. (Peters Paragraph [0003]).
Regarding claims 11 and 22, Lee discloses wherein the management component further comprises a computing component that is configured for computing the sensor data (Paragraphs [0002] and [0007] discuss a device that is worn by a baby, the device includes a temperature sensor, and optionally, a variety of other sensors, a network system, which connects the baby monitoring device with computers at home and hospital for detecting infection early in a microorganismic infection cycle.).
Lee does not explicitly disclose:
a computing component for adjusting the model.
Peters teaches:
a computing component for adjusting the model (Paragraphs [0003]-[0004], [0006], [0100]-[0101], and [0237] discuss classifying a medical condition of a mammal (e.g., a human) using predictive models, some machine-learning techniques determine a model for performing the desired task, the machine-learning model can be loaded by model evaluator to evaluate the model in classification system implemented by computers.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, a computing component for adjusting the model, as taught by Peters, in order to improve efficiency of the prediction. (Peters Paragraph [0003]).
Regarding claim 14, Lee discloses System for monitoring young children comprising (Paragraph [0015] discusses baby health monitoring system and method.):
a plurality of sensors providing signals that are configured to be converted into sensor data (Paragraphs [0022]-[0025] and [0068]-[0069] discuss baby monitoring device with sensors and data from sensors.);
a management component configured for receiving the sensor data and for managing a health status and or disease progression (Paragraphs [0060] discuss baby’s sensor data the condition of the baby can be read directly from the baby monitoring device and thus a parent or doctor may be immediately informed if any abnormal condition is detected, when an abnormal condition of a baby is detected, this can be transmitted online to a computer at hospital.).
Lee does not explicitly disclose:
managing a health status on the basis of a model.
Peters teaches:
managing a health status on the basis of a model (Paragraphs [0003] and [0006] discuss classifying a medical condition of a mammal (e.g., a human) using predictive models.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, managing a health status on the basis of a model, as taught by Peters, in order to improve efficiency of the prediction. (Peters Paragraph [0003]).
Regarding claim 25, Lee does not disclose computer related product for carrying out the method of claim 1.
Peters teaches:
computer related product for carrying out the method of claim 1 (Paragraph [0129] discusses computer program product can also be tangibly embodied in a computer- or machine-readable medium, such as the memory, the storage device, or memory on the processor.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, computer related product for carrying out the method of claim 1, as taught by Peters, in order to improve efficiency of the prediction. (Peters Paragraph [0003]).
Regarding claim 26, Lee does not explicitly disclose computer related product for carrying of claim 1 wherein the model is based on an explicable Al concept (AIX).
Peters teaches:
computer related product for carrying of claim 1 wherein the model is based on an explicable Al concept (AIX) (Paragraph [0021] discusses machine-learning model can include at least one of a classifier, an artificial neural network.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, computer related product for carrying of claim 1 wherein the model is based on an explicable Al concept (AIX), as taught by Peters, in order to improve efficiency of the prediction. (Peters Paragraph [0003]).
Claims 12-13 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Peters and in further view of Demmer (U.S. Pub. No. 2022/0211332 A1).
Regarding claims 12 and 23, Lee does not explicitly disclose wherein the management component further comprises a deciding component that is configured for initiating the managing component for managing the health status with a different complexity level.
Demmer teaches:
wherein the management component further comprises a deciding component that is configured for initiating the managing component for managing the health status with a different complexity level (Paragraphs [0006], [0068]-[0069] discuss monitoring a patient using a medical device, processing circuitry may process and analyze the sensor data using a scoring system. That is, points indicative of a risk-level of patient's health may be assigned to various types of sensor data and answers to questions, and processing circuitry may add the points to determine a risk-level of patient's health for a variety of conditions using the machine learning model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, wherein the management component further comprises a deciding component that is configured for initiating the managing component for managing the health status with a different complexity level, as taught by Demmer, in order to provide a more holistic and complete picture of patient status. (Demmer Paragraph [0006]).
Regarding claims 13 and 24, Lee does not explicitly disclose wherein the management component further comprises a deciding component that is configured for initiating the managing component for managing the health status with a different frequency.
Demmer teaches:
wherein the management component further comprises a deciding component that is configured for initiating the managing component for managing the health status with a different frequency (Paragraphs [0006], [0008], [0068]-[0069] discuss monitoring a patient using a medical device, processing circuitry may process and analyze the sensor data using a scoring system. That is, points indicative of a risk-level of patient's health may be assigned to various types of sensor data and answers to questions, and processing circuitry may add the points to determine a risk-level of patient's health for a variety of conditions using the machine learning model, monitoring, by the processing circuitry, the patient, where at least one of a period or a frequency of monitoring are based on the risk level of the patient's health.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, wherein the management component further comprises a deciding component that is configured for initiating the managing component for managing the health status with a different frequency, as taught by Demmer, in order to provide a more holistic and complete picture of patient status. (Demmer Paragraph [0006]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached on (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAWN T. HAYNES/
Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
Prosecution Insights