DETAILED ACTION
This action is made in response to the communication filed on August 8, 2024. This action is made non-final.
Claims 1-20 are pending. Claims 1, 8, and 15 are independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards a non-statutory embodiment.
As to claims 15-20, the claims are directed to a computer readable memory storage device. However, the broadest reasonable interpretation of a computer readable memory storage device includes non-transitory embodiments. Applicant is advised to amend the claims to recite “non-transitory” to best overcome this rejection.
Claims 1-20 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 1-7 recite a method of generating a recipe, which is within the statutory category of a process. Claims 8-14 recite a system for generating a recipe, which is within the statutory class of a machine. Claims 15-20 recite a computer readable memory for generating a recipe, which for the purposes of eligibility analysis will be considered as statutory, and is within the statutory class of a manufacture.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1-20, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The bolded limitations of:
Claims 1, 8, and 15 (claim 1 being representative)
receiving data for a patient having chronic kidney disease (CKD), the data including patient medical data and patient preference data; querying a large language model using the received data to generate a plurality of recipes; automatically verifying the generated recipes; submitting the generated recipes to a human user for verification; and providing the generated recipes for use based on both automated and human verification of the recipes.
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). For example, but for the noted computer elements, the claim encompasses a person following rules or instructions to generate a custom meal plan for a patient based on their medical records and preferences. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“processor” and “memory”—all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The “automated food preparation device” is not a generic computer component; however it is recited at a high levels of generality and similarly amount to generally linking the abstract idea to a particular technological environment. (See MPEP 2106.04(d)(1) indicating generally linking an abstract idea to a particular technological environment does not amount to integrating the abstract idea into a practical application).
Though the independent claims further recite querying a “large language model”, the limitation merely recites using large language model and is recited at a high level of generality without providing any details model is and similarly amounts to generally linking the abstract idea to a particular technological environment.
The claims only manipulate abstract data elements into another form. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“a processor”, “memory”—see Specification Fig. 1, [0027], [0028] describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements). Therefore, these additional elements amount 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 that amounts to significantly more. See MPEP 2106.05(f).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Furthermore, as discussed above, the additional element of a “automated food preparation device” is recited at high levels of generality and were determined to generally link the abstract idea into a particular technological environment or field of use. This additional element have been re-evaluated under step 2B and have also been found insufficient to provide significantly more. (See MPEP 2106.05(A) indicating generally linking an abstract idea to a particular technological environment does not amount to significantly more).
Similarly, as discussed above, querying “large language model” was recited at a high level of generality and determined to generally link the abstract idea to a particular technological environment. This additional element has been re-evaluated under step 2B and has also been found insufficient to provide significantly more. (See MPEP 2106.05(A) indicating generally linking an abstract idea to a particular technological environment does not amount to significantly more). Furthermore, the Background section of Applicant’s Specification (e.g., see [0023]) indicates that large language model are well-understood, routing, and conventional in the field. (See MPEP 2106.05(I)(A) indicating that well-understood, routine, and conventional activities cannot provide significantly more).
Dependent Claims
The limitations of dependent but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Claims 2, 10, 16 merely recite the type of medical data; Claims 3, 4, 11, 12, 17 merely recite further generating a shopping list; Claims 5, 13, 18 merely recites providing the recipe to a user to approve or modify; Claims 6, 14, 19 merely recite modifying the recipe based on patient feedback; Claims 7, 9, 20 merely recite providing the recipe to a food preparation device, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
As to claims 7, 9, and 17-20 reciting an LLM, computing device, and automated food preparation device, these additional elements are considered to “generally linking” and/or “apply it” under both the practical application and significantly more analysis, as detailed in the analysis above.
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.
Claim(s) 1-6 and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Avery et al. (USPPN: 2021/0241881; hereinafter Avery) in further view of Kohn (USPPN: 2024/0071599; hereinafter Kohn).
As to claim 1, Avery teaches A method of dietary management (e.g., see Title, Abstract), comprising:
receiving data for a patient having chronic kidney disease (CKD), the data including patient medical data and patient preference data (e.g., see Fig. 1, [0056], [0065], [0148], [0162] wherein patient data including preference data, electronic medical record data, and the therapeutic/nutrition therapy needs such as for a renal diet. Notably, the patient having “chronic kidney disease” is interpreted as an intended use statement. Applicant is remined that, typically, no patentable distinction is made by an intended use or result unless some structural difference is imposed by the use or result on the structure or material recited in the claim, or some manipulative difference is imposed by the use or result on the action recited in the claim. An intended use generally does not impart a patentable distinction if it merely states an intention or is a description of how the claimed method is to be used. (See MPEP 2111.05). In the present case, Avery, having taught receiving patient data having specific dietary and/or health-based needs, wherein one such need can be a renal diet, then it meets the claimed invention);
querying a large [language] model using the received data to generate a plurality of recipes (e.g., see [0150], [0153]-[0155] wherein a plurality of databases can be queried to generate recipes);
automatically verifying the generated recipes (e.g., see [0156] wherein the recipes can be automatically verified);
submitting the generated recipes to a human user for verification (e.g., see [0159], [0170] wherein the recipe can further be reviewed by a dietician or suitable reviewer); and
providing the generated recipes for use based on both automated and human verification of the recipes (e.g., see [0093], [0123] wherein the updated/confirmed menu/care plan is provided).
While Avery teaches querying a plurality of databases of different sources, Avery fails to teach querying a language model. However, in the same field of endeavor of personalized food and nutrition plans, Kohn teaches querying language models (e.g., see [0026]-[0033] wherein a plurality of data sources are analyzed for determining food/nutrient relationships, including the use of natural language processing models). Accordingly, it would have been obvious to modify Avery in view of Kohn with a reasonable expectation of success. One would have been motivated to make the modification in order to generate and search human language.
As to claim 2, the rejection of claim 1 is incorporated. Avery further teaches wherein the patient medical data includes at least one nutrient maximum and at least one nutrient minimum, and wherein the generated recipes include nutrient values that satisfy the at least one nutrient maximum and the at least one nutrient minimum (e.g., see [0148, [0158]] wherein the patient dietary rules can include various restrictions such as a low sodium, low fat and/or cholesterol diet, or minimum nutrition requirements).
As to claim 3, the rejection of claim 1 is incorporated. Avery-Kohn further teaches generating a shopping list of ingredients for the generated recipes (e.g., see [0172] wherein the items for the generated menu can be provided on an order list. While the limitation is taught in Avery, Kohn additionally taches providing a shopping list of the generated menu, see [0097]).
As to claim 4, the rejection of claim 3 is incorporated. Avery-Kohn further teaches wherein the shopping list is generated by a querying a large language model using the generated recipes (e.g., see [0172] wherein the items for the generated menu can be provided on an order list. While the limitation is taught in Avery, Kohn additionally taches providing a shopping list of the generated menu, see [0097]).
While Avery teaches querying a plurality of databases of different sources, Avery fails to teach querying a language model. However, in the same field of endeavor of personalized food and nutrition plans, Kohn teaches querying language models (e.g., see [0026]-[0033] wherein a plurality of data sources are analyzed for determining food/nutrient relationships, including the use of natural language processing models). Accordingly, it would have been obvious to modify Avery in view of Kohn with a reasonable expectation of success. One would have been motivated to make the modification in order to generate and search human language.
As to claim 5, the rejection of claim 1 is incorporated. Avery further teaches wherein submitting the generated recipes to a human user comprises: parsing the generated recipes into a standardized format (e.g., see [0195], [0214] wherein the generated recipes are provided in a suitable format); sending the generated recipes in the standardized format to a dietary expert (e.g., see [0221] wherein a dietician can be presented with the generated recipes on a user interface); and prompting the dietary expert to approve or modify the generated recipes (e.g., see [0222], [0225] wherein the dietician can make changes to the preliminary menu and/or confirm the menu).
As to claim 6, the rejection of claim 1 is incorporated. Avery-Kohn further teaches receiving patient feedback for the generated recipes (e.g., see [0111], [0180] wherein a patient can provide feedback for the generated recipes); and using the received patient feedback to query a large language model and generate a second plurality of recipes (e.g., see [0056], [0091], [0111], [0214] wherein the models are queries to generate new recommendations for the patient taking into account the patient feedback/preferences).
While Avery teaches querying a plurality of databases of different sources, Avery fails to teach querying a language model. However, in the same field of endeavor of personalized food and nutrition plans, Kohn teaches querying language models (e.g., see [0026]-[0033] wherein a plurality of data sources are analyzed for determining food/nutrient relationships, including the use of natural language processing models). Accordingly, it would have been obvious to modify Avery in view of Kohn with a reasonable expectation of success. One would have been motivated to make the modification in order to generate and search human language.
As to claims 15-19, the claims are directed to the computer-readable memory storage device implementing the method of claims 1, 2, 4-6, and are similarly rejected.
Claim(s) 7-14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Avery et al. (USPPN: 2021/0241881; hereinafter Avery) in further view of Kohn (USPPN: 2024/0071599; hereinafter Kohn) and Mossier et al. (USPPN: 2023/0298730; hereinafter Mossier)
As to claim 7, the rejection of claim 1 is incorporated. Avery further teaches converting each recipe from the plurality of recipes into instructions formatted for the use of an automated food preparation device (e.g., see [0195] wherein the recipes are provided in any suitable format. Notably “for the use of an automated food preparation device” is interpreted as an intended use statement. Applicant is remined that, typically, no patentable distinction is made by an intended use or result unless some structural difference is imposed by the use or result on the structure or material recited in the claim, or some manipulative difference is imposed by the use or result on the action recited in the claim. An intended use generally does not impart a patentable distinction if it merely states an intention or is a description of how the claimed method is to be used. (See MPEP 2111.05). In the present case, Avery, having taught sending the results in any suitable format, then it meets the claimed invention); and sending the formatted instructions for use in automatically preparing food (e.g., see [0195] wherein the results are provided. See comment above re: “for use in automatically preparing food”).
While the claim limitations of “for the use of an automated food preparation device” and “for use in automatically preparing food” are interpreted as intended use statements, for the purposes of compact prosecution and in the same filed of endeavor of providing nutritional regimens, Mossier teaches instructions formatted for the use of an automated food preparation device; and sending the formatted instructions for use in automatically preparing food (e.g., see [0044] wherein the proposed meals are provided as instructions to an automated food dispenser to prepare the meal servings). Accordingly, it would have been obvious to modify Avery-Kohn in view of Mossier with a reasonable expectation of success. One would have been motivated to make the modification to prepare food for patients through an automated process (e.g., see [0029] of Mossier).
As to claim 8, Avery teaches A dietary management system (e.g., see Title, Abstract), comprising:
a processor (e.g., see Fig. 6);
memory comprising instructions (e.g., see Fig. 6), which when executed by the processor cause the dietary management system to:
receive data for a patient having chronic kidney disease (CKD), the data including patient medical data and patient preference data (e.g., see Fig. 1, [0056], [0065], [0148], [0162] wherein patient data including preference data, electronic medical record data, and the therapeutic/nutrition therapy needs such as for a renal diet. Notably, the patient having “chronic kidney disease” is interpreted as an intended use statement. Applicant is remined that, typically, no patentable distinction is made by an intended use or result unless some structural difference is imposed by the use or result on the structure or material recited in the claim, or some manipulative difference is imposed by the use or result on the action recited in the claim. An intended use generally does not impart a patentable distinction if it merely states an intention or is a description of how the claimed method is to be used. (See MPEP 2111.05). In the present case, Avery, having taught receiving patient data having specific dietary and/or health-based needs, wherein one such need can be a renal diet, then it meets the claimed invention);
query a large [language] model using the received data to generate a plurality of recipes (e.g., see [0150], [0153]-[0155] wherein a plurality of databases can be queried to generate recipes);
automatically verify the generated recipes (e.g., see [0156] wherein the recipes can be automatically verified);
submit the generated recipes to a human user for verification (e.g., see [0159], [0170] wherein the recipe can further be reviewed by a dietician or suitable reviewer); and
based on both automated and human verification of the recipes, provide the generated recipes (e.g., see [0093], [0123] wherein the updated/confirmed menu/care plan is provided).
While Avery teaches querying a plurality of databases of different sources, Avery fails to teach querying a language model. However, in the same field of endeavor of personalized food and nutrition plans, Kohn teaches querying language models (e.g., see [0026]-[0033] wherein a plurality of data sources are analyzed for determining food/nutrient relationships, including the use of natural language processing models). Accordingly, it would have been obvious to modify Avery in view of Kohn with a reasonable expectation of success. One would have been motivated to make the modification in order to generate and search human language.
While Avery-Kohn teach providing the generated recipe for preparing the food, Avery-Kohn fail to teach provide the generated recipes to the automated food preparation device.
However, in the same filed of endeavor of providing nutritional regimens, Mossier teaches an automated food preparation device; and provide the generated recipes to the automated food preparation device for use in automatically preparing food (e.g., see [0044] wherein the proposed meals are provided as instructions to an automated food dispenser to prepare the meal servings. Notably “for the use of an automated food preparation device” is interpreted as an intended use statement. Applicant is remined that, typically, no patentable distinction is made by an intended use or result unless some structural difference is imposed by the use or result on the structure or material recited in the claim, or some manipulative difference is imposed by the use or result on the action recited in the claim. An intended use generally does not impart a patentable distinction if it merely states an intention or is a description of how the claimed method is to be used. (See MPEP 2111.05)). Accordingly, it would have been obvious to modify Avery-Kohn in view of Mossier with a reasonable expectation of success. One would have been motivated to make the modification to prepare food for patients through an automated process (e.g., see [0029] of Mossier).
As to claim 9, the rejection of claim 8 is incorporated. Avery-Mossier teach wherein the instructions further cause the dietary management system to convert the generated recipes into instructions formatted for the use of the automated food preparation device (e.g., see [0195], [0214] of Avery wherein the generated recipes are provided in a suitable format. See also [0044] of Mossier wherein the proposed meals are provided as instructions to an automated food dispenser). Accordingly, it would have been obvious to modify Avery-Kohn in view of Mossier with a reasonable expectation of success. One would have been motivated to make the modification to prepare food for patients through an automated process (e.g., see [0029] of Mossier).
As to claim 10, the rejection of claim 8 is incorporated. Avery further teaches wherein the patient medical data includes at least one nutrient maximum and at least one nutrient minimum, and wherein the generated recipes include nutrient values that satisfy the at least one nutrient maximum and the at least one nutrient minimum (e.g., see [0148, [0158]] wherein the patient dietary rules can include various restrictions such as a low sodium, low fat and/or cholesterol diet, or minimum nutrition requirements).
As to claim 11, the rejection of claim 8 is incorporated. Avery-Kohn further teaches generating a shopping list of ingredients for the generated recipes (e.g., see [0172] wherein the items for the generated menu can be provided on an order list. While the limitation is taught in Avery, Kohn additionally taches providing a shopping list of the generated menu, see [0097]).
As to claim 12, the rejection of claim 11 is incorporated. Avery-Kohn further teaches wherein the shopping list is generated by a querying a large language model using the generated recipes (e.g., see [0172] wherein the items for the generated menu can be provided on an order list. While the limitation is taught in Avery, Kohn additionally taches providing a shopping list of the generated menu, see [0097]).
While Avery teaches querying a plurality of databases of different sources, Avery fails to teach querying a language model. However, in the same field of endeavor of personalized food and nutrition plans, Kohn teaches querying language models (e.g., see [0026]-[0033] wherein a plurality of data sources are analyzed for determining food/nutrient relationships, including the use of natural language processing models). Accordingly, it would have been obvious to modify Avery in view of Kohn with a reasonable expectation of success. One would have been motivated to make the modification in order to generate and search human language.
As to claim 13, the rejection of claim 8 is incorporated. Avery further teaches wherein submitting the generated recipes to a human user comprises: parsing the generated recipes into a standardized format (e.g., see [0195], [0214] wherein the generated recipes are provided in a suitable format); sending the generated recipes in the standardized format to a dietary expert (e.g., see [0221] wherein a dietician can be presented with the generated recipes on a user interface); and prompting the dietary expert to approve or modify the generated recipes (e.g., see [0222], [0225] wherein the dietician can make changes to the preliminary menu and/or confirm the menu).
As to claim 14, the rejection of claim 8 is incorporated. Avery-Kohn further teaches receiving patient feedback for the generated recipes (e.g., see [0111], [0180] wherein a patient can provide feedback for the generated recipes); and using the received patient feedback to query a large language model and generate a second plurality of recipes (e.g., see [0056], [0091], [0111], [0214] wherein the models are queries to generate new recommendations for the patient taking into account the patient feedback/preferences).
While Avery teaches querying a plurality of databases of different sources, Avery fails to teach querying a language model. However, in the same field of endeavor of personalized food and nutrition plans, Kohn teaches querying language models (e.g., see [0026]-[0033] wherein a plurality of data sources are analyzed for determining food/nutrient relationships, including the use of natural language processing models). Accordingly, it would have been obvious to modify Avery in view of Kohn with a reasonable expectation of success. One would have been motivated to make the modification in order to generate and search human language.
As to claim 20, the claim is directed to the computer readable memory implementing the method of claim 7 and is similarly rejected.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STELLA HIGGS whose telephone number is (571)270-5891. The examiner can normally be reached Monday-Friday: 9-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Choi can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STELLA HIGGS/Primary Examiner, Art Unit 3681