DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been examined in this application. This communication is the first action on the merits.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA , the applicant, regards as the invention.
Claim 1, and similarly claims 14 and 20, recite “applying the nutritional prediction machine-learning model to output, based on at least one of: a first set of features for the user, a second set of features for a set of items, or a third set of features for a current session of the user, a vector of scores for the user, each score from the vector of scores indicative of a preference of the user for a respective nutritional attribute of the set of nutritional attributes; comparing each score from the vector of scores with a threshold score;” (emphasis added). As recited, this limitation is unclear as generally a MLM produces output, not applied to output. Consequently, one of ordinary skill in the art cannot determine how to avoid infringement of these claims because the metes and bounds of these claims are unclear. For examination purposes, the Examiner has interpreted this limitation as merely applying the nutritional prediction machine-learning model.
Claims 2-13 depend from claim 1 and thus inherit the deficiencies of claim 1.
Claims 15-19 depend from claim 14 and thus inherit the deficiencies of claim 14.
Claim 5, and similarly claim 18, recite “generating the user interface comprises updating a tag on the shelf with the label about the nutritional attribute associated with the item [emphasis added].” As recited, it is unclear how can the user interface can be a the user interface of the user when it is in relation to a shelf at the location of the source, which would typically be a user interface of the source. Consequently, one of ordinary skill in the art cannot determine how to avoid infringement of these claims because the metes and bounds of these claims are unclear. For examination purposes, the Examiner has interpreted this limitation as merely generating the user interface.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A – Prong One. If the claims fall within one of the statutory categories, it must then be determined whether the claims recite an abstract idea, law of nature, or natural phenomenon.
Step 2A – Prong Two. If the claims recite an abstract idea, law of nature, or natural phenomenon, it must then be determined whether the claims recite additional elements that integrate the judicial exception into a practical application. If the claims do not recite additional elements that integrate the judicial exception into a practical application, then the claims are directed to a judicial exception.
Step 2B. If the claims are directed to a judicial exception, it must be evaluated whether the claims recite additional elements that amount to an inventive concept (i.e. “significantly more”) than the recited judicial exception.
In the instant case, claims 1-13 are directed to a process; claims 14-19 are directed to a manufacture; and claim 20 is directed to a machine.
A claim “recites” an abstract idea if there are identifiable limitations that fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106. In the instant case, claim 1, and similarly claims 14 and 20, recites the steps of:
receiving a signal indicating interaction of the user; in response to the received signal, accessing a nutritional prediction model, wherein the nutritional prediction model is to predict preferences of the user for a set of nutritional attributes; applying the nutritional prediction model to output, based on at least one of: a first set of features for the user, a second set of features for a set of items, or a third set of features for a current session of the user, a vector of scores for the user, each score from the vector of scores indicative of a preference of the user for a respective nutritional attribute of the set of nutritional attributes; comparing each score from the vector of scores with a threshold score; responsive to a score from the vector of scores being greater than the threshold score, generating, based at least in part on the received signal, the user that includes a label about a nutritional attribute from the set of nutritional attributes associated with the score; and causing to display with the label about the nutritional attribute -- these claim limitations set forth certain methods of organizing human activity, particularly commercial interactions including advertising, marketing, and sales activities/behaviors.
Additionally, these steps set forth mental processes, particularly concepts performed in the human mind or by a human using a pen and paper, including, inter alia, the observation and evaluation of information.
Further, the limitations of the claims are not indicative of integration into a practical application. Taking the independent claim elements separately, the additional elements of performing the steps via a network from a device, with an online system, prediction machine-learning model of the online system, wherein the nutritional prediction machine-learning model is trained, a user interface of the device, the generated user interface -- merely implement the abstract idea on a computer environment. Additionally, taking the dependent claim elements separately, the additional elements of performing the steps via a database of the online system, one or more sensors, a dashboard, interfaces, and retraining the machine-learning model also merely implement the abstract idea on a computer environment. Considered in combination, the steps of Applicant’s method add nothing that is not already present when the steps are considered separately.
Thus, claims 1-20 are directed to an abstract idea.
Regarding the independent claims, the technical elements of performing the steps via a network from a device, with an online system, prediction machine-learning model of the online system, wherein the nutritional prediction machine-learning model is trained, a user interface of the device, the generated user interface -- merely implement the abstract idea on a computer environment. Additionally, regarding the dependent claims, the technical elements of performing the steps via a database of the online system, one or more sensors, a dashboard, interfaces, and retraining the machine-learning model also merely implement the abstract idea on a computer environment.
When considering the elements and combinations of elements, the claim(s) as a whole, do not amount to significantly more than the abstract idea itself. This is because the claims do not amount to an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment; the claims merely amounts to the application or instructions to apply the abstract idea on a computer; or the claims amounts to nothing more than requiring a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry.
The analysis above applies to all statutory categories of invention. Accordingly, claims 1-20 are rejected as ineligible for patenting under 35 USC 101 based upon the same rationale.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Guillemin (WO 2020163700 A1).
1. A method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
receiving, via a network from a device associated with a user of an online system, a signal indicating interaction of the user with the online system; (Guillemin: Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33)
in response to the received signal, accessing a nutritional prediction machine-learning model of the online system, wherein the nutritional prediction machine-learning model is trained to predict preferences of the user for a set of nutritional attributes; (Guillemin: Page 27, Ln 33 through Page 28, Ln 3) (By acquiring or, keeping track of past purchases, the HCMS 200 is learning the consumer’s shopping pattern over time and, therefore, estimates the food item retention more and more accurately.); Page 4; Lns 5-22 (receiving profile information of a consumer of the one or more items in the cart, the profile information including diagnostic information of at least one medical condition of the consumer; receiving dietary reference intake information of one or more nutrients, the dietary reference intake information including a recommended intake value for each nutrient of one or more nutrients;))
applying the nutritional prediction machine-learning model to output, based on at least one of: a first set of features for the user, a second set of features for a set of items, or a third set of features for a current session of the user, a vector of scores for the user, each score from the vector of scores indicative of a preference of the user for a respective nutritional attribute of the set of nutritional attributes; (Guillemin: Page 27, Ln 33 through Page 28, Ln 3) (By acquiring or, keeping track of past purchases, the HCMS 200 is learning the consumer’s shopping pattern over time and, therefore, estimates the food item retention more and more accurately.); Page 4; Lns 5-22 (receiving profile information of a consumer of the one or more items in the cart, the profile information including diagnostic information of at least one medical condition of the consumer; receiving dietary reference intake information of one or more nutrients, the dietary reference intake information including a recommended intake value for each nutrient of one or more nutrients); Page 25, Lns 24-33; Page 5; Ln 25 through Page 6; Ln 12 (receive dietary reference intake information of one or more nutrients, the dietary reference intake information including a recommended intake value for each nutrient of one or more nutrients; compute a daily consumption value for each item of the one or more items in the cart; generate, for each nutrient of the one or more nutrients, a nutrient score by adjusting the recommended intake value of the nutrient based at least on the diagnostic information of the at least one medical condition of the consumer))
comparing each score from the vector of scores with a threshold score; (Guillemin: Page 31, Ln 8 through Page 33, Ln 14 (The first step of the nudging process, performed as part of the step 540 in the method 500 shown in Fig. 7, is to identify every nutrient that is off target in the HC. That is done by looking up the nutrients whose score is less than the maximum value (e.g. 100), or lower than an arbitrary threshold (e.g. 80% of highest score). . . . In some embodiments, there is a method provided to analyze the scores of every nutrient in the HC. The weak nutrient scores, under an arbitrary threshold, allows the HCMS system 200 to identify the inadequate nutrients in the HC. The nutrient inadequacy comes from an inappropriate daily intake, either in excess, in the case of neutral or low-is-better nutrients, or in deficit, in case of neutral or high-is-better nutrients. The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Fig. 13A; Page 32; Lns 10-35 (The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.))
responsive to a score from the vector of scores being greater than the threshold score, generating, based at least in part on the received signal, a user interface of the device associated with the user that includes a label about a nutritional attribute from the set of nutritional attributes associated with the score; and (Guillemin: Fig. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 (The first step of the nudging process, performed as part of the step 540 in the method 500 shown in Fig. 7, is to identify every nutrient that is off target in the HC. That is done by looking up the nutrients whose score is less than the maximum value (e.g. 100), or lower than an arbitrary threshold (e.g. 80% of highest score). . . . In some embodiments, there is a method provided to analyze the scores of every nutrient in the HC. The weak nutrient scores, under an arbitrary threshold, allows the HCMS system 200 to identify the inadequate nutrients in the HC. The nutrient inadequacy comes from an inappropriate daily intake, either in excess, in the case of neutral or low-is-better nutrients, or in deficit, in case of neutral or high-is-better nutrients. The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 32; Lns 10-35 (The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 45, Lns 1-12)
causing the device associated with the user to display the generated user interface with the label about the nutritional attribute. (Guillemin: Figs. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 ((The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 45, Lns 1-12)
2. The method of claim 1, wherein:
receiving the signal comprises receiving, from the device associated with the user via the network, a request for an item; (Guillemin: Fig. 12A; Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33)
generating the user interface comprises generating the user interface that includes a tag with the nutritional attribute associated with the item; and (Guillemin: Figs. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 ((The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 45, Lns 1-12)
displaying the user interface comprises displaying the user interface that includes the tag with the nutritional attribute next to the item. (Guillemin: Figs. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 ((The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 45, Lns 1-12)
3. The method of claim 1, wherein:
receiving the signal comprises
receiving, from the device associated with the user via the network, a request for an item; (Guillemin: Fig. 12A; Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33)
generating the user interface comprises:
retrieving, from a catalog database of the online system and based on one or more scores from the vector of scores being greater than one or more threshold scores, an image associated with the item, and (Guillemin: Fig. 4; Fig. 12A-13A; Page 16, Lns 1-10 (databases); Page 17, Ln 26 through Page 18, Ln 14 (As shown in Fig. 4, the health cart management database 220 may obtain information from a variety of other databases. For example, in some embodiments, the health cart management system (HCMS) database 220 may receive or transmit information to one or more of a dietary reference intake sub model database 221, a nutrient coefficients sub model database 222, a food composition sub model database 223, or food taxonomy sub model database 224.); Page 31, Ln 8 through Page 33, Ln 14)
generating one or more nutritional labels associated with the one or more scores; and (Guillemin: Figs. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 ((The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 45, Lns 1-12)
displaying the user interface comprises displaying the retrieved image and the one or more nutritional labels at the user interface. (Guillemin: Figs. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 ((The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 45, Lns 1-12)
4. The method of claim 1, wherein:
receiving the signal comprises:
gathering, via one or more sensors mounted to a physical receptacle utilized by the user for shopping at a location of a source associated with the online system, data with information about an item, and (Guillemin: Page 2, Lns 17-30 (using the smartphone camera to scan barcodes of food and personal care products.); Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33)
receiving, from a computing system associated with the physical receptacle and via the network, the gathered data as the received signal; and (Guillemin: Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33)
generating the user interface comprises
generating, based at least in part on the gathered data, a message at a dashboard of the physical receptacle that includes the label about the nutritional attribute associated with the item. (Guillemin: Figs. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 ((The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 45, Lns 1-12)
5. The method of claim 1, wherein:
receiving the signal comprises:
gathering, via one or more sensors mounted to a physical receptacle utilized by the user for shopping at a location of a source associated with the online system, data with indication that the user is approaching an item placed at a shelf at the location of the source, and (Guillemin: Page 2, Lns 17-30 (using the smartphone camera to scan barcodes of food and personal care products.); Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33)
receiving, from a computing system associated with the physical receptacle and via the network, the gathered data as the received signal; and (Guillemin: Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33)
generating the user interface comprises updating a tag on the shelf with the label about the nutritional attribute associated with the item. (Guillemin: Figs. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 ((The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 45, Lns 1-12)
6. The method of claim 1, wherein receiving the signal comprises:
receiving, from the device associated with the user via the network, information that the user added an item to a cart, and the method further comprising: (Guillemin: Fig. 12A; Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33)
responsive to each score of a subset of scores from the vector of scores associated with the item being less than or equal to the threshold score, generating another user interface of the device associated with the user that includes an alert message for the user that the item is not consistent with the predicted preferences of the user; and (Guillemin: Fig. 13A; Page 31, Ln 8 through Page 33, Ln 14 (The first step of the nudging process, performed as part of the step 540 in the method 500 shown in Fig. 7, is to identify every nutrient that is off target in the HC. That is done by looking up the nutrients whose score is less than the maximum value (e.g. 100), or lower than an arbitrary threshold (e.g. 80% of highest score). . . . In some embodiments, there is a method provided to analyze the scores of every nutrient in the HC. The weak nutrient scores, under an arbitrary threshold, allows the HCMS system 200 to identify the inadequate nutrients in the HC. The nutrient inadequacy comes from an inappropriate daily intake, either in excess, in the case of neutral or low-is-better nutrients, or in deficit, in case of neutral or high-is-better nutrients. The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 32; Lns 10-35 (The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess.))
causing the device associated with the user to display the other user interface with the alert message. (Guillemin: Figs. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 ((The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit.))
7. The method of claim 1, wherein receiving the signal comprises:
gathering, via one or more sensors mounted to a physical receptacle utilized by the user for shopping at a location of a source associated with the online system, data with information about an item added into the physical receptacle, and the method further comprising: (Guillemin: Page 2, Lns 17-30 (using the smartphone camera to scan barcodes of food and personal care products.); Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33)
responsive to each score of a subset of scores from the vector of scores associated with the item being less than or equal to the threshold score, generating a user interface at a dashboard of the physical receptacle that includes an alert message for the user that the item is not consistent with the predicted preferences of the user; and (Guillemin: Fig. 13A; Page 31, Ln 8 through Page 33, Ln 14 (The first step of the nudging process, performed as part of the step 540 in the method 500 shown in Fig. 7, is to identify every nutrient that is off target in the HC. That is done by looking up the nutrients whose score is less than the maximum value (e.g. 100), or lower than an arbitrary threshold (e.g. 80% of highest score). . . . In some embodiments, there is a method provided to analyze the scores of every nutrient in the HC. The weak nutrient scores, under an arbitrary threshold, allows the HCMS system 200 to identify the inadequate nutrients in the HC. The nutrient inadequacy comes from an inappropriate daily intake, either in excess, in the case of neutral or low-is-better nutrients, or in deficit, in case of neutral or high-is-better nutrients. The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess.); Page 32; Lns 10-35 (The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess.))
causing the dashboard of the physical receptacle to display the user interface with the alert message. (Guillemin: Figs. 12A-13A; Page 31, Ln 8 through Page 33, Ln 14 ((The icon 1010 in the Fig. 13A is an exemplar representation of neutral or low-is-better nutrients in excess. The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit.))
8. The method of claim 1, wherein: receiving the signal comprises
receiving, from the device associated with the user via the network, a search query entered by the user via a search interface of the device; and (Guillemin: Figs. 12A-13B; Page 31, Lns 10-28 (The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess. If the user touches or clicks the icon 1020 then a list of food categories 1025a and 1025b rich in the selected nutrient in deficit (potassium is the considered example) is displayed in the user interface 1000B as shown in Fig. 13B. The user can then select a food category to get a list of food rich in the selected nutrient. The list of food categories is determined by utilizing a specific food taxonomy configured in the HCMS whose exemplar is sketched in the Fig. 20B; the list of food categories is placed under a parent node representing a nutrient. The exemplar food taxonomy 2020 shows that, for instance, starchy vegetables 2121 and bread 2120 are two categories rich in carbohydrate 2110.);
generating the user interface comprises:
retrieving, from a catalog database of the online system and based on the search query, the set of items, (Guillemin: Figs. 12A-13B; Page 31, Lns 10-28 (The icon 1020 in the Fig. 13A is an exemplar representation of neutral or high-is-better nutrients in deficit. Activating the icon 1010 will list the food items in the shopping cart contributing the most to the content of the nutrient in excess. If the user touches or clicks the icon 1020 then a list of food categories 1025a and 1025b rich in the selected nutrient in deficit (potassium is the considered example) is displayed in the user interface 1000B as shown in Fig. 13B. The user can then select a food category to get a list of food rich in the selected nutrient. The list of food categories is determined by utilizing a specific food taxonomy configured in the HCMS whose exemplar is sketched in the Fig. 20B; the list of food categories is placed under a parent node representing a nutrient. The exemplar food taxonomy 2020 shows that, for instance, starchy vegetables 2121 and bread 2120 are two categories rich in carbohydrate 2110.); Page 16, Lns 1-10; Page 17, Ln 26 through Page 18, Ln 14; Page 31, Ln 8 through Page 33, Ln 14))
ranking, based at least in part on the vector of scores, the set of items to generate a ranked list of items, and (Guillemin: Page 45, Lns 1-30 (For the sake of convenience, the HCMS system 200 allows the list of substitutes to be sorted by different means, like by nutritional impact - making the nutrient within, or the closest to, target - by price or by other criteria such as personal food preference among others. It is to be noticed that combining different sorting is configurable in the HCMS system 200, including external criteria such as defined by grocers aiming at incenting shoppers to purchase certain targeted food items.); Page 33, Ln 28- Page 34, Ln 10)
selecting, from the ranked list of items, a subset of items for presentation to the user; and displaying the user interface comprises displaying the user interface with the subset of items and information about one or more nutritional attributes for each of the subset of items. (Guillemin: Page 45, Lns 1-30 (For the sake of convenience, the HCMS system 200 allows the list of substitutes to be sorted by different means, like by nutritional impact - making the nutrient within, or the closest to, target - by price or by other criteria such as personal food preference among others. It is to be noticed that combining different sorting is configurable in the HCMS system 200, including external criteria such as defined by grocers aiming at incenting shoppers to purchase certain targeted food items.); Page 33, Ln 28- Page 34, Ln 10)
9. The method of claim 8, wherein selecting the subset of items comprises:
filtering, based at least in part on the vector of scores, one or more items from the ranked list of items to generate the subset of items. (Guillemin: Page 45, Lns 1-30 (For the sake of convenience, the HCMS system 200 allows the list of substitutes to be sorted by different means, like by nutritional impact - making the nutrient within, or the closest to, target - by price or by other criteria such as personal food preference among others. It is to be noticed that combining different sorting is configurable in the HCMS system 200, including external criteria such as defined by grocers aiming at incenting shoppers to purchase certain targeted food items.); Page 33, Ln 28- Page 34, Ln 10)
10. The method of claim 1, further comprising:
retrieving, from a catalog database of the online system, the first set of features including at least one of information about a purchase history for the user, information about nutritional labels of items previously purchased by the user, and a set of health attributes from past search queries entered by the user via the device associated with the user; (Guillemin: Page 25, Lns 1-33 (purchase history))
retrieving, from the catalog database, the second set of features including at least one of nutritional information for the set of items and information about ingredients for the set of items; and (Guillemin: Page 25, Lns 1-33 (ingredients))
receiving, from the device associated with the user via the network, the third set of features including one or more features of a source associated with the current session of the user and information about a type of shopping associated with the current session of the user. (Guillemin: Page 16, Lns 22-30 (The HCMS system 200 accesses the data from the health cart management database 220 to evaluate, inform, and provide guidance to food shoppers regarding the "healthfulness" of the items they are pushing into their shopping cart, either online or at the grocery store.); Page 4, Lns 5-22 (receiving item information of one or more items in the cart, the item information including a quantity of each item of the one or more items in the cart and a sharing parameter for at least one item of one or more items in the cart); Page 25, Lns 24-33; Figs. 12A-13B; Page 31, Lns 10-28; Page 16, Lns 1-10; Page 17, Ln 26 through Page 18, Ln 14; Page 31, Ln 8 through Page 33, Ln 14))
11. The method of claim 1, further comprising:
retrieving, from a catalog database of the online system, data including at least one of a collection of profiles for a collection of users of the online system, search history for the collection of users, and purchase history for the collection of users; (Guillemin: Page 25, Lns 1-33 (purchase history of users))
generating training data by assigning labels to nutritional attributes associated with the retrieved data; and (Guillemin: Page 25, Lns 1-33 (data of items in purchase history)
training, using the training data, the nutritional prediction machine-learning model to generate a set of initial values for a set of parameters of the nutritional prediction machine-learning model. (Guillemin: Page 25, Lns 1-33 (data of items in purchase history used)
12. The method of claim 1, further comprising:
retrieving, from a catalog database of the online system, data including a collection of profiles for a collection of users of the online system, the collection of profiles including information about preferences of the collection of users for nutritional attributes; and (Guillemin: Page 25, Lns 24-33; Figs. 12A-13B; Page 31, Lns 10-28; Page 16, Lns 1-10; Page 17, Ln 26 through Page 18, Ln 14; Page 31, Ln 8 through Page 33, Ln 14))
training, using the retrieved data, the nutritional prediction machine-learning model to generate a set of initial values for a set of parameters of the nutritional prediction machine-learning model. (Guillemin: Page 25, Lns 24-33; Figs. 12A-13B; Page 31, Lns 10-28; Page 16, Lns 1-10; Page 17, Ln 26 through Page 18, Ln 14; Page 31, Ln 8 through Page 33, Ln 14))
13. The method of claim 1, further comprising:
collecting feedback data with information about engagement by the user with one or more items for which information about one or more nutritional attributes is displayed at the user interface; and (Guillemin: Page 25, Lns 24-33; Figs. 12A-13B; Page 31, Lns 10-28; Page 16, Lns 1-10; Page 17, Ln 26 through Page 18, Ln 14; Page 31, Ln 8 through Page 33, Ln 14))
re-training the nutritional prediction machine-learning model by updating, using the collected feedback data, a set of parameters of the nutritional prediction machine-learning model. (Guillemin: Page 25, Lns 24-33; Figs. 12A-13B; Page 31, Lns 10-28; Page 16, Lns 1-10; Page 17, Ln 26 through Page 18, Ln 14; Page 31, Ln 8 through Page 33, Ln 14))
Conclusion
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/JENNIFER V LEE/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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