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 .
This communication is in response to the amendment received on 07/13/2026. Claims 1-9, 11-19 and 21-22 remain pending in this application.
The 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph rejection of the claims has been withdrawn in light of the amendments.
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 1-9, 11-19 and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-9, 21 are drawn to a system which is within the four statutory categories (i.e. machine). Claims 11-19, 22 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
Claims 1 and 11 have been amended to recite “identify a nutrition requirement for a user as a function of a user attribute, wherein the nutrition requirement includes information on a plurality of nutrients; receive, from a monitoring device, a monitoring element; generate a modified nutrition requirement as a function of the monitoring element, wherein the modified nutrient requirement includes a modification in quantity of at least one nutrient of the plurality of nutrients;…identify an aliment that fulfills the modified nutrition requirement…”.
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].
The amended claims 1 and 11 also recite the limitations of “correlate, using a trained machine-learning model, the modified nutrition requirement with a user affliction of the user attribute” and “identifying the aliment comprises: providing the modified nutrition requirement and the user affliction to a classifier trained using a nutrition training set relating aliments to user attributes”, which correspond to mathematical relationships, which falls within the “mathematical concepts” grouping of abstract ideas. In particular, the current specification recites “As used in this disclosure a “classifier” is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine-learning algorithm known as a “classification algorithm,” as described below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith.” in [0015] and “Still referring to FIG. 2, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data 204 may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 204 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 204 according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below.” in [0026].
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 2-9, 12-19 and 21-22 are ultimately dependent from claims 1, 11 and include all the limitations of claims 1, 11. Therefore, claims 2-9, 12-19 and 21-22 recite the same abstract idea. Claims 2-9, 12-19 and 21-22 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 identifying, correlating and generating 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/presenting 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, correlating and generating 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 § 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 1-5, 7-8, 11-15, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (CN111161842A) in view of Seidensticker (US 2023/0162617 A1).
As per claims 1-5, 7-8, 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 has been amended to recite a method for presenting an aliment from a modified nourishment scheme, wherein the method comprises:
identifying, at a computing device, a nutrition requirement for a user as a function of a user attribute, wherein the nutrition requirement includes information on a plurality of nutrients (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 and “…sending the acquired data to a postoperative rehabilitation nutrition suggestion model, and processing all data of the user by the postoperative rehabilitation nutrition suggestion model, wherein in the step, the construction process of the postoperative rehabilitation nutrition suggestion model of the patient is specifically as follows: dividing the data obtained in the step 1 and the step 2 into two parts, wherein one part is used as a training set; meanwhile, the collected operation conditions, postoperative detection indexes, personal information, eating habits, eating taboos, current nutrition conditions and nutrition records in the rehabilitation process of patients who are subjected to different operations are used as the input of the model, the rehabilitation conditions of the patients are used as the output, and an artificial intelligent algorithm is used for obtaining a postoperative rehabilitation nutrition suggestion model through continuous learning and training; the other part is used as a test set for verifying the accuracy of the model;…” on page 2, lines 32-38);
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-detection indexes comprising body temperature etc. on page 2, lines 57-59);
generating, at the computing device, a modified nutrition requirement as a function of the monitoring element, wherein the modified nutrient requirement includes a modification in quantity of at least one nutrient of the plurality of nutrients (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 target nutrient to the proposed model processing result comparing with the patient-user and to display to the user…” in abstract);
correlating, using a trained machine-learning model, the modified nutrition requirement with a user affliction of the user attribute (Liu discloses “…dividing the data obtained in the step 1 and the step 2 into two parts, wherein one part is used as a training set; meanwhile, the collected operation conditions, postoperative detection indexes, personal information, eating habits, eating taboos, current nutrition conditions and nutrition records in the rehabilitation process of patients who are subjected to different operations are used as the input of the model, the rehabilitation conditions of the patients are used as the output, and an artificial intelligent algorithm is used for obtaining a postoperative rehabilitation nutrition suggestion model through continuous learning and training; the other part is used as a test set for verifying the accuracy of the model; then, adjusting and optimizing the model so as to establish a postoperative rehabilitation nutrition suggestion model;…” on page 2, lines 33-41);
identifying, at the computing device, an aliment that fulfills the modified nutrition requirement, wherein identifying the aliment comprises:
providing the modified nutrition requirement and a user affliction to a classifier trained using a nutrition training set relating aliments to user attributes; and outputting the aliment using the classifier (Liu discloses “…comparing the processing result of the postoperative rehabilitation nutrition suggestion model with the rehabilitation target of the patient user and displaying the result to the user…” in abstract and “providing nutrition suggestion model-thorough learning and training-inputting the operation condition of the user” on page 2, lines 32-40).
Liu fails to expressly teach “presenting, using a graphical user interface, the aliment as a function of the monitoring element, wherein presenting the aliment comprises presenting a nourishment value corresponding to the aliment”. However, this feature is well known in the art, as evidenced by Seidensticker.
In particular, Seidensticker discloses “…The example user interface may also provide functionalities (e.g., data fields, tabbed options) for the user to select responses for various user attributes… The user can then select calculate 514 to determine the nutrient level. In some aspects, the user may be able to avail themselves of a more accurate or comprehensive assessment of a nutrient level and/or a dietary intake recommendation through an advanced version 516 of the application. The advanced version may prompt entry of response for significantly more user attributes.” in [0049].
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to include the aforementioned limitation as disclosed by Seidensticker with the motivation of “to determine user intent and to customize the user interface of the application” (Seidensticker; [0049]).
Claim 12 has been amended to recite the method of claim 11, wherein identifying the nutrition requirement includes receiving the user attribute, and wherein the user attribute comprises user input, the user input comprising a health condition (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 has been amended to recite the method of claim 11, 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 has been amended to recite the method of claim 11, 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 has been amended to recite the method of claim 11, wherein identifying the nutrition requirement includes receiving a nutrition training set that correlates at least an aliment 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 has been amended to recite the method of claim 11, wherein generating the 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).
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 has been amended to recite the method of claim 11, wherein identifying the aliment further comprises hierarchically sorting a plurality of aliments.
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.
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (CN111161842A) in view of Bennett et al. (hereinafter Bennett) (CN107845414B).
The newly added claims 21/22 recites the system/method of claim 1/11, wherein identifying the aliment further comprises: creating a distance metric from the nourishment value to each candidate aliment of a plurality of candidate aliment, and selecting at least an aliment of the plurality of candidate aliments as a function of a minimal distance metric calculation.
Liu fails to expressly teach “identifying the aliment further comprises: creating a distance metric from the nourishment value to each candidate aliment of a plurality of candidate aliment, and selecting at least an aliment of the plurality of candidate aliments as a function of a minimal distance metric calculation”. However, this feature is well known in the art, as evidenced by Bennett.
In particular, Bennett discloses “The processor 105 is configured to determine an index based on the nutrient content of the food consumed ("consumption profile") and the target nutrient level ("target profile"). As used herein, the phrase "based on" means "based at least in part on. The metric profile is determined based on the user's goals and/or selected diet plan. The index indicates how much the consumption profile (determined by the level of nutrients in the food consumed) deviates from the target profile. The index may be a numerical index (such as a number between 1 and 100 or 0 and 1), an alphabet index (such as a scale from F to a +), a color selected from a color gradient representing a range of indices, a graphical icon indicating progress towards the metric profile, a combination of these, or any other visual or audible indicator conveying the degree of deviation between the consumption profile and the metric profile…” on page 14, lines 23-28 and “…the system includes means for generating a plurality of candidate meals, each candidate meal including one or more candidate foods; and means for selecting at least one recommended meal from the plurality of candidate meals based on a comparison between the amount of the first nutrient in each candidate meal and the deviation of the first nutrient in the at least one deviation profile…” on page 10, lines 6-10.
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to include the aforementioned limitation as disclosed by Bennett with the motivation of “provide candidate nutrients that impact the nutritional index” (Bennett; page 6, lines 14-24).
Response to Arguments
Applicant's arguments filed 07/13/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Arguments about 35 USC 101 rejection:
Applicant argues that claims have been amended to recite the limitations of “receiving a monitoring element from a monitoring device, generating a modified nutrition requirement as a function of the monitoring element using a trained machine-learning model, and presenting an aliment through a graphical user interface as a function of the monitoring element. And Applicant argues that acquisition of physiological data from a monitoring device and the machine-driven computation and presentation of nutritional output do not fall within any grouping of the certain methods of organizing human activity.
In response, Examiner submits that the limitation of “generating a modified nutrition requirement as a function of the monitoring element using a trained machine-learning model” is not directed to certain methods of human activity, but is directed to mathematical relationships. And the limitation of “presenting an aliment through a graphical user interface as a function of the monitoring element” is not part of the abstract idea rejection. This limitation corresponds to insignificant extra-solution activity, as indicated in the updated rejection above.
The limitations of “identify a nutrition requirement for a user as a function of a user attribute, wherein the nutrition requirement includes information on a plurality of nutrients; receive, from a monitoring device, a monitoring element; generate a modified nutrition requirement as a function of the monitoring element, wherein the modified nutrient requirement includes a modification in quantity of at least one nutrient of the plurality of nutrients;…identify an aliment that fulfills the modified nutrition requirement…” correspond to an abstract idea of “certain methods of organizing human activity” with a recitation of generic computing and monitoring devices. This is a method of managing interactions between people, such as user following rules and instructions relating to identifying aliment/nutrition requirements for the patient using generic devices. The mere nominal recitation of a generic computing device does not take the claims out of the methods of organizing human interactions grouping.
Applicant argues that the current claims recite “using a trained machine-learning model” to correlate the modified nutrition requirement corresponds to an improvement to the technology, since the system uses machine-learning process and training data, similar to the claims of Desjardins case.
In response, Examiner submits that Desjardin decision recites “Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id.”. In the current application, the specification does not provide any improvement to the technology or the AI system, it basically recites using the machine learning model/classifier and trained data to obtain results.
Applicant argues that the amended claims recite an ordered combination of elements that together supply an inventive concept, similar to Bascom claims.
In response, Examiner submits that July 2015 Update to subject matter eligibility guidance states that “When identifying abstract ideas, examiners should keep in mind that judicial exceptions need not be old or long-prevalent, and that even newly discovered judicial exceptions are still exceptions, despite their novelty”. The Bascom decision recites “the patent describes its filtering system as a novel advance over prior art computer filters, in that no one had previously provided customized filters at a remote server. Bascom’s specific method of filtering Internet content found to be non-conventional and non-generic.”. There is no indication in the current application that a non-conventional or non-generic usage of the technology.
Applicant argues that claims recite a machine learning pipeline: defined inputs comprising the modified nutrition training set relating aliments to user attributes, and a defined output comprising the aliment, which do not correspond to a well-understood, routine and conventional activity.
In response, Examiner submits that the MPEP recites:
“Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).”.
The current claims are directed to limitations that are generally linking the use of the judicial exception to a particular technological environment or field of use, and claim steps amounts to no more than mere instructions to apply the exception using a generic computer component (as indicated in the rejection above). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Arguments about 35 USC 102 rejection:
Applicant argues that Liu does not teach “monitoring element received from a monitoring device”.
In response, Examiner submits that Liu teaches “…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, and Liu also teaches “acquiring…detection indexes comprising body temperature, electrocardiogram, blood pressure and blood sugar content…”,. on page 2, lines 57-59. Examiner considers that the detection indexes are acquired from a device, such as phone or smart watch (monitoring device), since the body temperature, blood pressure or blood sugar content only can be acquired from a monitoring device.
Applicant argues that Liu does not teach “providing the modified nutrition requirement and a user affliction to a classifier trained using a nutrition training set relating aliments to user attributes; and outputting the aliment using the classifier “.
In response, Examiner submits that Liu discloses “…comparing the processing result of the postoperative rehabilitation nutrition suggestion model with the rehabilitation target of the patient user and displaying the result to the user…” in abstract and “step 3, sending the acquired data to a postoperative rehabilitation nutrition suggestion model, and processing all data of the user by the postoperative rehabilitation nutrition suggestion model, wherein in the step, the construction process of the postoperative rehabilitation nutrition suggestion model of the patient is specifically as follows: dividing the data obtained in the step 1 and the step 2 into two parts, wherein one part is used as a training set; meanwhile, the collected operation conditions, postoperative detection indexes, personal information, eating habits, eating taboos, current nutrition conditions and nutrition records in the rehabilitation process of patients who are subjected to different operations are used as the input of the model, the rehabilitation conditions of the patients are used as the output, and an artificial intelligent algorithm is used for obtaining a postoperative rehabilitation nutrition suggestion model through continuous learning and training; the other part is used as a test set for verifying the accuracy of the model; then, adjusting and optimizing the model so as to establish a postoperative rehabilitation nutrition suggestion model; after that, inputting the operation condition of the user, various detection indexes after the operation, personal information eating habits, eating taboos and the current nutrition condition into a postoperative rehabilitation nutrition suggestion model for processing” on page 2, lines 32-40.
Applicant argues that Liu does not teach generating, at the computing device, a modified nutrition requirement as a function of the monitoring element, wherein the modified nutrient requirement includes a modification in quantity of at least one nutrient of the plurality of nutrients”.
In response, Examiner submits that 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 target nutrient to the proposed model processing result comparing with the patient-user and to display to the user…” in abstract.
Therefore, the arguments are not persuasive.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687
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