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 .
Status of Claims
This action is in reply to an application filed on 07/20/2023. Claims 1-10 are currently pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 8 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention.
Claim 8 recites selecting a substitute food "when the effect on the health condition of the user estimated by the estimation unit exceeds a predetermined degree of effect." The specification supplies no metric by which the degree of effect is measured or against which it is compared. Its only guidance is by relative example, at ¶ 0053:
"For example, when a disease that the user U1 may suffer from when he/she ingests a purchase-candidate food is serious or when the probability of the onset of a disease is very high, the selection unit 16 selects, as an alternative food, a substitute food for the purchase-candidate food."
"Serious" and "very high" are themselves terms of degree, and the effect may be either an inferred disease (claim 5) or a probability of its occurrence (¶ 0041) — quantities that are not commensurable. One of ordinary skill would not be apprised of the scope with reasonable certainty. MPEP § 2173.05(b). Clarification is required.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
The following limitations invoke 35 U.S.C. § 112(f). Each uses the generic placeholder "unit," modified by functional language, without reciting sufficient structure to perform the recited function. "Unit" is a nonce term that serves as a substitute for "means." MPEP § 2181(I)(A).
Limitation
Spec Support
Corresponding Structure/Algorithm
"first acquisition unit configured to acquire information about a user…" (cl. 1, 9, 10)
¶¶ 0036-0037, 0057
Acquires the information transmitted from the user terminal 20 (Step S101).
"second acquisition unit configured to acquire information about at least one purchase-candidate food" (cl. 1)
¶¶ 0036-0037, 0057
Algorithm at ¶ 0042: "in the database 40_1, a food purchase history and changes in the health condition of each of a plurality of the users (subjects) in the past are registered. The estimation unit 13 extracts, from the database 40_1, a food purchase history and changes in the health condition of at least one subject whose health condition has been determined to be similar to that of the current user U1," and estimates by referring to the extracted record.
"estimation unit configured to estimate an effect on the health condition…" (cl. 1)
¶¶ 0038-0043, 0059
Algorithm at ¶¶ 0045-0046: "in the database 40_2, diseases that a plurality of past users (subjects) suffered from respectively, and costs that were incurred to treat the diseases are registered. The calculation unit 14 extracts, from the database 40_2, a cost that was incurred to treat a disease of at least one of the subjects who suffered from the disease that the user U1 may suffer from," and calculates by referring to the extracted cost.
"calculation unit configured to calculate a cost…" (cl. 1)
¶¶ 0044-0047, 0062
Algorithm at ¶¶ 0045-0046: "in the database 40_2, diseases that a plurality of past users (subjects) suffered from respectively, and costs that were incurred to treat the diseases are registered. The calculation unit 14 extracts, from the database 40_2, a cost that was incurred to treat a disease of at least one of the subjects who suffered from the disease that the user U1 may suffer from," and calculates by referring to the extracted cost.
"output unit configured to output…" (cl. 1)
¶¶ 0048-0049, 0065
Outputs the estimation and calculation results, transferred to the user terminal 20 through the network 50.
"selection unit configured to select, as an alternative food, a substitute food…" (cl. 8)
¶¶ 0052-0053
Selects a substitute food when the estimated effect exceeds a predetermined degree of effect.
The corresponding structure for each is the health management support apparatus 10, a computer executing a control program — the specification states at ¶ 0070 that "some or all of the control processes performed in the health management support system 1 can be implemented by having a CPU (Central Processing Unit) execute a computer program" — programmed to perform the algorithm identified above for that unit.
No rejection under § 112(b) for lack of corresponding structure is made. Although the disclosure of a general-purpose computer alone would be insufficient for a computer-implemented limitation under § 112(f), MPEP § 2181(II)(B), the specification discloses an algorithm for each recited unit at the paragraphs identified above. Applicant may rebut the § 112(f) interpretation by showing that a limitation recites sufficient structure, by amending to recite structure, or by amending to invoke § 112(f) expressly. MPEP § 2173.05(g).
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 1-10 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 — Statutory Category
Claims 1-8 recite a system (machine); claim 9 recites a method (process); claim 10 recites a non-transitory computer readable medium (manufacture). Each falls within a statutory category. The analysis proceeds to Step 2A.
Step 2A, Prong One — The Claims Recite an Abstract Idea
Claim 9 is taken as representative. It recites acquiring information about a user including a health condition; acquiring information about a purchase-candidate food; estimating an effect on the health condition caused by ingesting that food; calculating a cost incurred to alleviate the estimated effect; and outputting the estimated effect and the calculated cost.
These limitations recite a mental process. But for the recitation of generic computer components, each step is one that can be performed in the human mind or with pen and paper. A dietitian or pharmacist can be told a customer’s health condition, look at the item the customer is holding, judge from experience that ingesting it may bring on a particular disease, consult a published fee schedule for the cost of treating that disease, and state both conclusions aloud. Observation, evaluation, judgment and opinion are mental processes. MPEP § 2106.04(a)(2)(III); Electric Power Group v. Alstom, 830 F.3d 1350, 1353-54 (Fed. Cir. 2016).
In the alternative, the claim recites certain methods of organizing human activity — both a commercial interaction in the nature of sales activity and marketing, and the management of personal behaviour. MPEP § 2106.04(a)(2)(II). The specification confirms this characterization. Its stated object at ¶ 0005 is to show "an economic risk caused by a disease that the user may suffer from when he/she purchases a purchase-candidate food, to thereby urge the user to purchase foods suitable for him/her," and ¶ 0050 repeats that the system "can urge the user U1 to purchase foods suitable for him/her."
Step 2A, Prong Two — No Integration Into a Practical Application
The additional elements are the recited units, the databases 40_1 and 40_2, the user terminal 20, the network 50, and, in claim 10, the non-transitory computer readable medium. Each is generic. The specification describes the units as implemented by "having a CPU (Central Processing Unit) execute a computer program" (¶ 0070), and describes the medium by a list of commodity storage — "a random-access memory (RAM), a read-only memory (ROM), a flash memory, a solid-state drive (SSD)… a CD-ROM, a digital versatile disc (DVD), a Blu-ray disc" (¶ 0071). The specification names no particular hardware, no special-purpose processor, and no non-conventional arrangement of components.
These additional elements amount to mere instructions to apply the exception using generic computer components. MPEP § 2106.05(f). The two acquiring steps are insignificant extra-solution data gathering and the outputting step is insignificant post-solution activity. MPEP § 2106.05(g); OIP Technologies v. Amazon.com, 788 F.3d 1359, 1363 (Fed. Cir. 2015).
No improvement to the functioning of a computer or to any other technology is claimed or described. MPEP § 2106.05(a). The advance the specification asserts is not technical. The problem it identifies with the prior art, at ¶ 0004, is that the prior system "does not show an economic risk caused by a disease that may occur when a purchase-candidate food is purchased" — a deficiency in what information is presented to a person, not in how any machine operates. The improvement asserted is accordingly an improvement in human decision-making.
Step 2B — Not Significantly More
The additional elements, considered individually and as an ordered combination, do not amount to significantly more than the judicial exception. Receiving data over a network, storing and retrieving records from a database, and displaying a result on a terminal are well-understood, routine and conventional activities previously known to the industry. MPEP § 2106.05(d)(II)(i), (iv); buySAFE v. Google, 765 F.3d 1350, 1355 (Fed. Cir. 2014); Alice Corp. v. CLS Bank, 573 U.S. 208, 225-26 (2014). Factual support is found in the specification itself, which discloses the computing environment only at the level of a CPU executing a program (¶ 0070) and a list of commodity storage media (¶ 0071), and which describes the databases 40_1 and 40_2 purely in terms of what records they hold (¶¶ 0042, 0045). Berkheimer v. HP, 881 F.3d 1360, 1369 (Fed. Cir. 2018).
The ordered combination adds nothing beyond the sum of its parts: data is gathered, an inference is drawn, a figure is looked up, and both are displayed.
Independent claims 1 and 10 recite the system and non-transitory computer readable medium for performing the method of claim 9 and are rejected for the same reasons.
III. Dependent Claims 2-8
Claims 2-4 narrow only the content of the data evaluated — the user attributes considered (cl. 2), the foods to be avoided (cl. 3), the food type and nutrient (cl. 4). Narrowing the subject matter of an abstract idea does not remove the claim from the exception. MPEP § 2106.04.
Claim 5— Prong One. Claim 5 recites that "the estimation unit infers a disease that the user may suffer from as the effect on the health condition of the user." Inferring the disease a person may develop is the diagnostic and prognostic judgment of a physician, and it is performed in the human mind. The specification confirms that the inference is of that ordinary kind. At ¶ 0039: "when the purchase-candidate food is an allergen food such as an egg, the estimation unit 13 infers an occurrence of a food allergy as an effect on the health condition of the user U1," and "when the purchase-candidate food is a fatty food, the estimation unit 13 infers an onset of a metabolic syndrome." A person told that a customer is allergic to eggs, and shown a carton of eggs, reaches the first conclusion unaided. Observation, evaluation, judgment and opinion are mental processes. MPEP § 2106.04(a)(2)(III)(B). Claim 5 therefore recites the exception more explicitly than claim 1 does.
Claim 5 — Prong Two and Step 2B. Claim 5 adds no additional element at all. It introduces no component, no data source, and no step beyond the estimation already recited in claim 1; it only specifies the form the estimate takes. There is accordingly nothing further to evaluate for integration into a practical application, and nothing that could supply an inventive concept. The additional elements remain those of claim 1 — the units, implemented by "having a CPU (Central Processing Unit) execute a computer program" (¶ 0070) — and they remain generic. Where a dependent claim recites only further detail of the judicial exception, the eligibility analysis of the parent controls. MPEP § 2106.04.
Claim 6 — Prong One. Claim 6 recites that the estimation unit "extracts, from among food purchase histories and changes in health conditions of a plurality of subjects in the past registered in a first database, a food purchase history and a change in health condition of at least one subject whose health condition has been determined to be similar to that of the user," and "estimates the effect on the health condition of the user by referring to the extracted food purchase history and the change in the health condition of the subject." Setting aside the database, these are steps of comparison and inference by analogy: identify people like this person, observe what happened to them, and conclude that the same may happen here. That is the ordinary reasoning of a clinician or an epidemiologist, and it is performed in the human mind. Observation, evaluation, judgment and opinion are mental processes. MPEP § 2106.04(a)(2)(III)(B). Claim 6 thus recites more of the abstract idea than claim 1, not less.
Claim 6 — Prong Two and Step 2B. The only additional element added by claim 6 is the first database. The specification describes it solely by the records it holds: "in the database 40_1, a food purchase history and changes in the health condition of each of a plurality of the users (subjects) in the past are registered" (¶ 0042), and it "may also" hold the age, gender, height and weight of those subjects so that the estimation unit "can easily narrow down the conditions" (¶ 0043). No structure, organization, indexing or access method is described, and no improvement in database technology is asserted. The claim is accordingly unlike Enfish v. Microsoft, 822 F.3d 1327 (Fed. Cir. 2016), where the claimed self-referential table was itself the asserted technical advance. Extracting records is data gathering in aid of the mental step and is insignificant extra-solution activity, MPEP § 2106.05(g), and storing and retrieving information in memory is well-understood, routine and conventional, MPEP § 2106.05(d)(II)(iv).
The recited similarity is a result, not a means. Claim 6 requires a subject "whose health condition has been determined to be similar to that of the user" but recites no rule, metric or threshold by which similarity is determined, and the specification supplies none — its only guidance is that additional demographic fields "may also be registered" so that conditions can be "narrow[ed] down" (¶ 0043). A claim that recites the desired outcome of a comparison without reciting how the comparison is carried out is not directed to a specific technological implementation. Contrast McRO v. Bandai Namco, 837 F.3d 1299, 1313 (Fed. Cir. 2016), where the claimed specific rules were the improvement. MPEP § 2106.05(a), (f).
Claim 7 — Prong One. Claim 7 recites that the calculation unit extracts, from records of "diseases that a plurality of past subjects suffered from respectively, and costs that were incurred to treat the diseases," the cost incurred for the disease the user may suffer from, and "calculates, by referring to the extracted cost," the cost that is incurred to treat that disease. Again setting aside the database, this is the ordinary practice of estimating what something will cost by reference to what comparable cases have cost — the everyday judgment of an actuary, a claims adjuster, or a billing clerk. It is a mental process of evaluation and judgment, MPEP § 2106.04(a)(2)(III)(B), and it is in addition a fundamental economic practice, being the pricing of an individual risk from pooled experience. MPEP § 2106.04(a)(2)(II)(A). The step of "calculat[ing] by referring to the extracted cost" is not further specified; no formula, weighting or adjustment is recited.
Claim 7 — Prong Two and Step 2B. The only additional element added by claim 7 is the second database, described in the same manner as the first — by the records it holds (¶ 0045) and by the optional demographic fields that let the calculation unit "easily narrow down the conditions" (¶ 0047). The analysis of claim 6 applies without change: retrieval of stored records is extra-solution data gathering and a well-understood, routine and conventional computer function. MPEP § 2106.05(d)(II)(iv), (g).
Claim 8 — Prong One. Claim 8 recites two further steps of the exception. First, it recites a comparison: the substitute is selected "when the effect on the health condition of the user estimated by the estimation unit exceeds a predetermined degree of effect." Comparing an estimate against a threshold to decide whether to act is evaluation and judgment. Second, it recites the selection of "a substitute food for at least one of the at least one purchase-candidate food." Proposing a different food in place of one a shopper has chosen is the everyday judgment of a dietitian, a nutritionist, or a shop assistant, and it is performed in the human mind. MPEP § 2106.04(a)(2)(III)(B). Claim 8 also re-recites the estimation of claim 1, since the estimated effect is the predicate for the comparison. The specification describes the step at the same level of generality, at ¶ 0053: "when a disease that the user U1 may suffer from when he/she ingests a purchase-candidate food is serious or when the probability of the onset of a disease is very high, the selection unit 16 selects, as an alternative food, a substitute food for the purchase-candidate food."
The substitution is a result, not a means. Claim 8 requires that a "substitute food" be selected but recites no criterion by which a food qualifies as a substitute — not nutritional equivalence, not category membership, not a comparative score, not availability. The specification supplies none either. Paragraph 0053, the only substantive description of the selection unit, states only the circumstances in which a substitute is selected and never how one is chosen; ¶ 0052 adds only that the apparatus "further includes a selection unit 16" as a block of the modified apparatus 10a. As with the similarity determination of claim 6, the claim recites the outcome of a judgment without reciting how the judgment is made, and so is not directed to a specific technological implementation. MPEP § 2106.05(a), (f); contrast McRO, 837 F.3d at 1313.
Claim 8 — Prong Two and Step 2B. The only additional element added by claim 8 is the selection unit, which is not a new component in any meaningful sense: it is a further functional block of the same computer already relied upon for the other units, implemented by "having a CPU (Central Processing Unit) execute a computer program" (¶ 0070). The further recitation that "the output unit further outputs information about the alternative food" adds no new element at all — it is the output unit of claim 1, performing the same function on an additional item of data, and displaying a result is insignificant post-solution activity. MPEP § 2106.05(g). Claim 8 therefore neither integrates the exception into a practical application nor supplies an inventive concept.
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 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Nakagawa, et al. (US 2023/0027710 A1) in view of Wayman, et al. (US 2015/0206450 A1) in further view of Sato, et al. (US 2021/0326998 A1).
With regards to claim 9, Nakagawa teaches a method for controlling a health management support system, comprising: acquiring information about a user, including a health condition of the user (¶¶ 0028, 0030-0031, 0035-0036, acquires a health related data group 16 and symptom data 18 for a customer, and customer data 32 is input to the model); acquiring information about at least one purchase… food (¶¶ 0039-0040, 0051, 0077, acquires purchase data 15 identifying purchased foods by JICFS item category through a POS system); estimating an effect on the health condition of the user that is caused when the user ingests the at least one purchase-candidate food (applies predictive model 27 (¶¶ 0061, 0064, 0066) to output a health predictive value 34 — "the having-disease rate" in percent, "a value indicating the possibility and/or the extent that the customer is affected by a specific disease" (¶ 0036). Nakagawa states that foods and beverages are best suited to the correlation because they "more directly reflect the influence of the health condition," and that the model is built from approximately 30,000 subjects (¶ 0026)); …and outputting the estimated effect on the health condition of the user… (¶¶ 0072-0074, 0080, outputs the prediction through output I/F 44 by printing a receipt 34, displaying on a display 52, or notifying a smartphone 38);
Nakagawa does not explicitly teach …-candidate; calculating a cost that is incurred to alleviate the estimated effect on the health condition of the user; …and the calculated cost.
Wayman teaches …-candidate (a shopper 122 uses a mobile scanner 120 to "scan a universal product code (UPC) 118 on a food item 116 on the shelves of the grocery store 114" before purchase (¶ 0034), and in a further embodiment generates a list of food items the shopper "is going to purchase in a future trip" (¶ 0021)). It would have been obvious before the effective filing date to apply Nakagawa’s health prediction to an item the shopper is currently selecting, as taught by Wayman, rather than only to items already purchased. Nakagawa supplies the reason: it contacts customers to "point out the possibility of being affected to propose an employable procedure while the person of an ordinary life is not aware of the disease or an exposed symptom is mild" (¶ 0037). Delivering that warning while the shopper still holds the item serves that stated purpose more directly than delivering it after checkout. Wayman confirms the benefit, stating that the disclosure is "attempting to ‘nudge’ the shopper 122 to make healthier substitutions of various food items the shopper 122 is selecting to buy" (¶ 0023), and contemplates the same delivery points as Nakagawa — before the trip (¶ 0021) and at the shelf (¶ 0034). MPEP § 2143(I)(C).
Sato teaches calculating a cost that is incurred to alleviate the estimated effect on the health condition of the user (medical expense predicting unit 113 predicts future medical expenses from the onset probability estimated by risk estimating unit 112 (¶¶ 0039, 0055-0057). Hospitalization medical expenses are computed as "one-time medical expenses + one-day hospital stay medical expenses × length of stay" (¶¶ 0058-0059) and annual outpatient expenses from the number of hospital visits per year (¶ 0062); the products are then summed across diseases at steps S303-S307 (¶¶ 0064-0066). The diseases are coronary artery and cerebrovascular events (¶ 0051)); …and the calculated cost (output apparatus 13 (¶¶ 0075, 0077) displays the estimated onset risks (¶ 0083, FIG. 7E) and the predicted future medical expenses (¶ 0085, FIG. 8A)). It would further have been obvious to include, in the information presented to the customer, the medical cost associated with the predicted disease, as taught by Sato. Sato teaches that a lay person cannot otherwise assess future medical expenses and that population averages do not account for the individual (¶¶ 0005, 0013), and that because "medical expenses needed when a disease develops" is not individual-dependent, "an average amount of medical expenses can be easily obtained from… previous data on statements of medical expenses, statistical data compiled by the Ministry of Health, Labour and Welfare, and the like" (¶ 0013). Attaching a monetary figure to Nakagawa’s having-disease rate makes the predicted risk concrete to the customer, which is the behaviour change Nakagawa seeks. This is the use of a known technique — deriving a medical expense from a disease onset probability — to improve a similar system in the same way. MPEP § 2143(I)(C), (D).
Claims 1 and 10
Claim 1 recites the system corresponding to claim 9. Under the § 112(f) interpretation set out above, the corresponding structure is a computer executing a control program. Nakagawa discloses information processing device 30 comprising CPU 41, input I/F 42 and storage device 43 (¶¶ 0069-0071) and output I/F 44 (¶¶ 0072-0074), corresponding to the first acquisition unit, second acquisition unit, estimation unit and output unit. Sato discloses analyzer 11 comprising data acquiring unit 110, risk estimating unit 112 and medical expense predicting unit 113, realized by a CPU executing a program (¶ 0039), corresponding to the calculation unit. Claim 10 is met because Nakagawa discloses the processes as a control program 26 executed by a CPU and stored in a storage device (¶¶ 0061, 0064), and Sato discloses a non-transitory computer readable medium storing the program (¶ 0021). Both are rejected on the rationale of claim 9.
With regards to claim 2, Nakagawa teaches the health management support system according to claim 1, wherein the information about the user includes information about at least one of an age, a gender, a height, and a weight of the user (¶ 0053, data indicating an age layer may be included and that gender data may be used as the explanatory variable).
With regards to claim 3, Wayman teaches the health management support system according to claim 1, wherein the information about the user includes information about at least either of a food that the user is recommended to refrain from ingesting and a food that the user is prohibited to ingest (shopper profile in DB 106 includes "any food allergies" (¶ 0016), and the score vector includes a score for allergens contained in the food item and for the likelihood of being part of dietary restrictions (¶ 0019)). The motivation to combine Wayman with Nakagawa is the same as stated in the above rejection to claim 9.
With regards to claim 4, Wayman teaches the health management support system according to claim 1, wherein the information about the at least one purchase-candidate food includes information about a type of the at least one purchase-candidate food and a nutrient contained therein (scoring is based on nutritional content including artificial flavoring and artificial ingredients (¶¶ 0018, 0041), and items are classified by food group (¶¶ 0014, 0019), with examples including red meat (¶ 0023). The motivation to combine Wayman with Nakagawa is the same as stated in the above rejection to claim 9.
With regards to claim 5, Nakagawa teaches the health management support system according to claim 1, wherein the estimation unit infers a disease that the user may suffer from as the effect on the health condition of the user (outputs a having-disease rate in percent indicating the possibility that the customer is affected by a specific disease (¶¶ 0036-0037)).
With regards to claim 6, Nakagawa teaches the health management support system according to claim 1, wherein the estimation unit extracts, from among food purchase histories and changes in health conditions of a plurality of subjects in the past registered in a first database, a food purchase history and a change in health condition of at least one subject whose health condition has been determined to be similar to that of the user, and the estimation unit estimates the effect on the health condition of the user by referring to the extracted food purchase history and the change in the health condition of the subject (Training data 12 comprises, for each of a plurality of subjects, purchase data 15 indicating a purchase history and symptom data 18 indicating the extent of that subject’s health symptom (¶¶ 0014, 0028, 0031, 0035, 0039). A target customer whose purchase pattern matches a given layer is predicted to share that layer’s health symptom).
With regards to claim 7, Sato teaches the health management support system according to claim 1, wherein the calculation unit extracts, from among diseases that a plurality of past subjects suffered from respectively, and costs that were incurred to treat the diseases, registered in a second database, a cost that was incurred to treat a disease of at least one of the subjects who suffered from the disease that the user may suffer from, inferred as the effect on the health condition of the user by the estimation unit, and the calculation unit calculates, by referring to the extracted cost, a cost that is incurred to treat the disease that the user may suffer from, inferred by the estimation unit (The average medical expense per disease is obtained from "previous data on statements of medical expenses, statistical data compiled by the Ministry of Health, Labour and Welfare" (¶ 0013), database 10 stores data on statements of medical expenses (¶ 0034), and values are extracted for the disease and for the subject’s gender and age and applied in the hospitalization and outpatient equations (¶¶ 0058-0059, 0062-0063)). The motivation to combine Sato with Nakagawa is the same as stated in the above rejection to claim 9.
With regards to claim 8, Nakagawa teaches the health management support system according to claim 1, further comprising …when the effect on the health condition of the user estimated by the estimation unit exceeds a predetermined degree of effect… (identifying customers who presented a having-disease rate "equal to a predetermined threshold value or higher" (¶ 0037))
Furthermore, Wayman teaches …a selection unit configured to select, as an alternative food, a substitute food for at least one of the at least one purchase-candidate food … wherein the output unit further outputs information about the alternative food (determines a healthier food item in the same food group having a higher score and provides it as the healthier food purchase suggestion (¶¶ 0022, 0028, 0044-0046)). The motivation to combine Wayman with Nakagawa is the same as stated in the above rejection to claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Laborne, et al. (US 2021/0391054 A1) which discloses a health cart management system may be configured to generate a health cart score based on items in a grocery cart. The system may receive item information including a quantity and a sharing parameter for an item in the cart. The system may receive profile information including medical conditions of a consumer of the items. The system may receive dietary reference intake information of nutrients, compute a daily consumption value for each item in the cart, and generate a nutrient score for each nutrient. The system may generate the nutrient score by adjusting the recommended intake value of the nutrient based on the consumer's medical conditions, and computing a daily nutrient consumption value for the nutrient based on the daily consumption quantity values for all items in the cart. The system may generate the health cart score by computing a weighted mean of the nutrient scores for all nutrients.
Wala, et al. (US 11,037,681 B1) which discloses an apparatus and method for informed personal-well-being decision making that provides a user with alerts and information, focused on health and wellness, on items they choose for possible consumption. Some embodiments include optical, sonic, smell and other sensors, communications with databases that identify ingredients and effects on health and well-being, as well as user inputs. From user input, GPS, local conditions and alerts, some embodiments determine information specific to the user and their environment. By using established, and creating new, databases, some embodiments compile, compare, transmit and store data on various consumables. Some embodiments provide access to information on the companies, manufacturers, and various other components in an item's trip from dirt to table. Some embodiments establish methods and procedures to ascertain both the point-of-origin and where the consumable has traveled. Some embodiments provide a score for the specified consumable to show the quality of health provided by the consumable.
M. A. Serhani, A. Ahmad, A. Alkhatri, M. Almansoori and A. Alkhyeli, "Non-Invasive Health Control through Guided Shopping: A Rule-Based Approach," 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco, 2019, pp. 1-8 which discloses monitoring life-long diseases involves not only direct observation and control of patient's vital signs but also the monitoring and the control of the lifestyle and food intake that is proven to have a direct impact on the fluctuation of these vital signs. In this paper, we propose an approach to complement the preventive health monitoring with reactive and proactive monitoring by controlling patient's food intake through customized, entertained, informative guidance, and recommendations that fit their health profile. We adopt a data mining approach to classify different health profiles, and develop two algorithms: the first algorithm supports profile and class constructions, automatic recommendation, and personalization of advices and the second algorithm tailors portion consumption to manage the food intake in an effective way. We also develop a mobile-based virtual reality application to support shopping and make the person experience entertaining. We evaluate our monitoring approach on different health profiles and the results suggest that our guided proactive health monitoring and recommendations lead to a better management of chronic diseases.
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/JOSEPH D BURGESS/ Primary Examiner, Art Unit 3685
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