DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
In particular, claims 1 and 11 recite “blocking nourishment contained within the modified nutrition requirement as a function of the nourishment value.”, and the specification fails to describe how the system blocks nourishment contained within the modified nutrition requirement as a function of the nourishment value.
Claims 2-10 and 12-20 inherit the deficiencies of claims 1, 11 through dependency and are therefore also rejected.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-10 are drawn to a system which is within the four statutory categories (i.e. machine). Claims 11-20 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
Claims 1 and 11 recite “identify a nutrition requirement for a user; receive, from a monitoring device, a monitoring element; generate a modified nutrition requirement as a function of the monitoring element; and identify an ailment that fulfills the modified nutrition requirement, wherein identifying the ailment comprises: determining a nourishment value corresponding to the modified nutrition requirement; and blocking nourishment contained within the modified nutrition requirement as a function of the nourishment value”. These limitations correspond to an abstract idea of certain methods of organizing human activity. This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic computing device does not take the claims out of the methods of organizing human interactions grouping. Thus, the claims recite an abstract idea.
The computing device and monitoring device described in the current specification as generic computing devices. For instance, the current specification recites “Computing device 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Computing device 104 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices.” in [0009] and “…As used in this disclosure “monitoring device” is an electronic device that is worn on the person of a user, such as without limitation close to and/or on the surface of the skin, wherein the device can detect, analyze, and transmit information concerning a body signal such as a vital sign, and/or ambient datum, wherein allowing immediate biofeedback to be sent to the user wearing the device. For example and without limitation, a monitoring device may include, without limitation, any device that further collects, stores, and analyzes data associated with monitoring elements. As a further non-limiting example, a monitoring device may consist of near-body electronics, on-body electronics, in-body electronics, electronic textiles, smart watches, smart glasses, smart clothing, fitness trackers, body sensors, wearable cameras, head-mounted displays, body worn cameras, Bluetooth headsets, wristbands, smart garments, chest straps, sports watches, fitness monitors, and the like thereof. As a further non-limiting example, a monitoring device may include earphones, earbuds, headsets, bras, suits, jackets, trousers, shirts, pants, socks, bracelets, necklaces, brooches, rings, jewelry, AR HMDs, VR HMDs, exoskeletons, location trackers, and gesture control wearables. As a further non-limiting example, a monitoring device may consist of, without limitation an Apple watch, Galaxy watch, FitBit Sense, Fossil Gen 5, Tag Heuer Connected, Garmin Instinct, and the like thereof.” in [0019].
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Claims 2-10 and 12-20 are ultimately dependent from claims 1, 11 and include all the limitations of claims 1, 11. Therefore, claims 2-10 and 12-20 recite the same abstract idea. Claims 2-10 and 12-20 describe a further limitation regarding the basis for generating nutrition requirements for a user. These are all just further describing the abstract idea recited in claims 1, 11, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “a computing device”, “a monitoring device”, using the computing device to perform generating, determining, identifying and blocking steps, which are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Claims also recite other additional limitations beyond abstract idea, including functions such as receiving data from/to a database, outputting data are insignificant extra-solution activities (see MPEP 2106.05 (g)), which do not provide a practical application for the abstract idea.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computing device to perform identifying, generating, determining and blocking steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-8, 10-15, 17-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu (CN111161842A).
As per claims 1-5, 7-8, 10, they are system claims which repeat the same limitations of claims 11-15, 17-18, 20, the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Tran disclose the underlying process steps that constitute the methods of claims 11-15, 17-18, 20, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 1-5, 7-8, 10 are rejected for the same reasons given below for claims 11-15, 17-18, 20.
Claim 11 recites a method for presenting an ailment from a modified nourishment scheme, wherein the method comprises:
identifying, at a computing device, a nutrition requirement for a user (Liu discloses “…step 1, obtaining the operation situation of the user operation after each detection index, personal information and the recovery target, step 2, obtaining the food habit of user, diet tabu and current nutritional status…” in abstract);
receiving, at the computing device and from a monitoring device, a monitoring element (Liu discloses “…step 1, obtaining the operation situation of the user operation after each detection index, personal information and the recovery target, step 2, obtaining the food habit of user, diet tabu and current nutritional status…” in abstract);
generating, at the computing device, a modified nutrition requirement as a function of the monitoring element (Liu discloses “…the obtained data is sent to the postoperative rehabilitation nutrition suggested model, postoperative rehabilitation nutrition suggested model for all the user data for processing, step 4, post-operative rehabilitation rehabilitation target nutrient to the proposed model processing result comparing with the patient-user and to display to the user…” in abstract); and
identifying, at the computing device, an ailment that fulfills the modified nutrition requirement, wherein identifying the ailment comprises:
determining a nourishment value corresponding to the modified nutrition requirement (Liu discloses “…the invention claims a method based on artificial intelligence technology of postoperative rehabilitation nutritional suggestion method, by means of artificial intelligence algorithm, for comprehensive processing and analysis to data of the patient user, rehabilitation provide reasonable nutrition suggested scheme after the operation of the patient, so as to ensure the energy supply patient rehabilitation activities, providing nutritional support to promote the recovery of patient body function.…” on page 2, par. 2); and
blocking nourishment contained within the modified nutrition requirement as a function of the nourishment value (Liu discloses “…the collected through different operation of the operation situation of the patient, post-operative detection index, personal information, dietary habits, diet contraindication, current nutritional status and nutrient recording the rehabilitation process as the input of the model. the recovery condition of these patients as output, using artificial intelligence techniques such as artificial neural network, support vector machine or other means, by continuously learning and training to obtain postoperative rehabilitation nutrition suggested model; the other part as the test set for validating the accuracy of the model; after adjusting and optimizing the model so as to establish the postoperative rehabilitation nutrient proposed model, thereafter, the operation condition of the patient user, operation after each detection index, personal information, eating habits, diet contraindication and current nutritional status input postoperative rehabilitation nutrition suggested model to process.…” on page 3, par. 3).
Claim 12 recites the method of claim 11, wherein identifying the nutrition requirement includes receiving a user attribute (Liu discloses “…step 1, obtaining the operation situation of the user operation after each detection index, personal information and the recovery target, step 2, obtaining the food habit of user, diet tabu and current nutritional status…” in abstract).
Claim 13 recites the method of claim 12, wherein the user attribute further comprises a user aliment history; and wherein identifying the nutrition requirement further comprises identifying the nutrition requirement as a function of the user aliment history (Liu discloses “…step 1, obtaining the operation situation of the user operation after each detection index, personal information and the recovery target, step 2, obtaining the food habit of user, diet tabu and current nutritional status…” in abstract).
Claim 14 recites the method of claim 12, wherein receiving the user attribute further comprises receiving a user vigor status, and wherein identifying the nutrition requirement further comprises identifying the nutrition requirement as a function of the user vigor status (Liu discloses “…step 1, obtaining the operation situation of the user operation after each detection index, personal information and the recovery target, step 2, obtaining the food habit of user, diet tabu and current nutritional status…” in abstract).
Claim 15 recites the method of claim 11, wherein identifying the nutrition requirement includes receiving a nutrition training set that correlates at least an ailment to a user attribute (Liu discloses “…step 1, obtaining the operation situation of the user operation after each detection index, personal information and the recovery target, step 2, obtaining the food habit of user, diet tabu and current nutritional status…” in abstract).
Claim 17 recites the method of claim 11, wherein generating a modified nutrition requirement includes receiving a modification training set (Liu discloses “…the collected through different operation of the operation situation of the patient, post-operative detection index, personal information, dietary habits, diet contraindication, current nutritional status and nutrient recording the rehabilitation process as the input of the model. the recovery condition of these patients as output, using artificial intelligence techniques such as artificial neural network, support vector machine or other means, by continuously learning and training to obtain postoperative rehabilitation nutrition suggested model; the other part as the test set for validating the accuracy of the model; after adjusting and optimizing the model so as to establish the postoperative rehabilitation nutrient proposed model, thereafter, the operation condition of the patient user, operation after each detection index, personal information, eating habits, diet contraindication and current nutritional status input postoperative rehabilitation nutrition suggested model to process.…” on page 3, par. 3).
Claim 18 recites the method of claim 17, wherein the modification training set includes a plurality of entries; and each entry of the plurality of entries correlates at least a monitoring element to a nutrition outcome (Liu discloses “…obtaining the food habit of user, diet tabu and current nutritional status; step 3, the obtained data is sent to the postoperative rehabilitation nutrition suggested model, postoperative rehabilitation nutrition suggested model for all the user data for processing, step 4, post-operative rehabilitation target nutrient to the proposed model processing result comparing with the patient-user and to display to the user; step 5, carrying out operation according to the processing result to the user after rehabilitation nutrient proposed model recommendation, step 6, dynamically adjusting the duration of and the subsequent post-operative nutritional suggested activities. recovery compared with the existing technology, the invention uses artificial intelligence algorithm for associated data analysis, for operation of the patient after healing and provide reasonable function of nutritional support and reference…” in abstract).
Claim 20 recites the method of claim 11, wherein blocking nourishment further comprises identifying a temporal element relating to the blocking (Liu discloses “…the collected through different operation of the operation situation of the patient, post-operative detection index, personal information, dietary habits, diet contraindication, current nutritional status and nutrient recording the rehabilitation process as the input of the model. the recovery condition of these patients as output, using artificial intelligence techniques such as artificial neural network, support vector machine or other means, by continuously learning and training to obtain postoperative rehabilitation nutrition suggested model; the other part as the test set for validating the accuracy of the model; after adjusting and optimizing the model so as to establish the postoperative rehabilitation nutrient proposed model, thereafter, the operation condition of the patient user, operation after each detection index, personal information, eating habits, diet contraindication and current nutritional status input postoperative rehabilitation nutrition suggested model to process.…” on page 3, par. 3).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 6, 9, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (CN111161842A) in view of Devries et al. (hereinafter Devries) (US 2017/0249445 A1).
As per claims 6, 9, they are system claims which repeat the same limitations of claims 16, 19, the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Tran disclose the underlying process steps that constitute the methods of claims 16, 19, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 6, 9 are rejected for the same reasons given below for claims 16, 19.
Claim 16 recites the method of claim 11, wherein identifying the nutrition requirement further comprises receiving a user affliction as an input and outputting an ailment wherein the ailment relates to the user affliction.
Claim 19 recites the method of claim 11, wherein identifying the ailment further comprises hierarchically sorting a plurality of ailments.
Liu fails to expressly teach “identifying the nutrition requirement further comprises receiving a user affliction as an input and outputting an ailment wherein the ailment relates to the user affliction” and “identifying the ailment further comprises hierarchically sorting a plurality of ailments”. However, this feature is well known in the art, as evidenced by Devries.
In particular, Devries discloses “…in combination with or in lieu of nutrition - related metrics, any health - related metrics can be predicted, for example: metrics reflecting the state of health and / or wellness; metrics reflecting the state of exercise, stress, and / or sleep; metrics reflecting the diagnosis and / or progress of a condition, illness, infection and / or disease, etc. In certain embodiments, in combination with or in lieu of nutrition - related metrics and / or health - related metrics, any metrics in general can be predicted e.g. specific activities and / or behaviours of the user, effects of the environment on the user, etc.” in par. 0054.
It would have been obvious to one of ordinary skill in the art to include in the nutrition advice system of Liu the ability to provide metrics reflecting the progress of condition, illness, infection, disease etc. as taught by Devries since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
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/DILEK B COBANOGLU/ Primary Examiner, Art Unit 3687