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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-11 and 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al (Non-Patent Literature entitled “Building a case-based diet recommendation system without a knowledge engineer”) in view of Deptford et al (Non-Patent Literature entitled “Cost of the Diet: a method and software to calculate the lowest cost of meeting recommended intakes of energy and nutrients from local foods”), and Nag et al (Non-Patent Literature entitled “Live Personalized Nutrition Recommendation Engine”).
Khan teaches a system for identifying compatible meal options, the system comprising a processor (inherent of “computer-based diet construction system”, §1, and required to provide the interface shown in Figure 4) wherein the processor is configured to:
receive a user biological marker, wherein the user biological marker comprises physiological data of a user (user data, e.g., diabetes or garlic allergy, §4.1 and Figure 3);
determine a food tolerance score as a function of the user biological marker, wherein the food tolerance score relates to a user ability to tolerate a food item (diet requirements, e.g., low sugar content due to diabetes, §4.1);
generate a food tolerance instruction set as a function of the food tolerance score (adaptation actions, e.g., increasing/decreasing portion size, Figure 4);
Khan fails to distinctly disclose:
receive a geofence, wherein the geofence comprises a predetermined geographic area selected by the user;
identify statistical makeup data as a function of the geofence;
generate a micronutrient band as a function of the statistical makeup data;
generate alimentary data as function of the micronutrient band, wherein the alimentary data comprises a recommended nutrient intake; and
identify one or more meal options as a function of the food tolerance instruction set and the alimentary data.
It is noted that Khan teaches modifying the menu generated for each user (e.g., Figure 3) by using rules (Figures 4 and 5). A rule is acquired when a modification action is performed and a corresponding user explanation is provided for why the action is necessary (pg. 168).
However, Deptford teaches a method of calculating the lowest cost of meeting recommended intakes of energy and nutrients from local foods (Abstract).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Khan’s system via rules such that affordable local foods are recommended over non-local foods in order to select the lease expensive combination of local foods that meets specifications for energy, macronutrients and micronutrients (Abstract) while considering local dietary habits (Implementation, pg. 2).
The combination of Khan and Deptford as defined above teaches:
receive a geofence, wherein the geofence comprises a predetermined geographic area selected by the user (“an area in which people have a similar diet, such as a province or district, an urban or peri-urban area, an agro-ecological zone or a livelihood zone, especially when a household economy approach has recently been done”, pg. 3 of Deptford);
identify statistical makeup data as a function of the geofence (“Data on the foods consumed by people in the locality of an assessment are collected in the field…Each food is then identified in the food database in the software by selecting either the example that is geographically closest to the assessment site or the generic CotD food…The list of foods can then be printed by the software and used to record current and retrospective prices in all seasons during a market survey”, pg. 3 of Deptford);
generate a micronutrient band as a function of the statistical makeup data (“[allowing] users to identify the micronutrients that most influence the cost of the diet… in order to minimize the risk of deficiency”, Vitamins and mineral specifications, pg. 6).
generate alimentary data as function of the micronutrient band, wherein the alimentary data comprises a recommended nutrient intake (preventing the software from exceeding specifications for micronutrients into toxic levels, Upper limits for specific nutrients, pg. 6); and
identify one or more meal options as a function of the food tolerance instruction set and the alimentary data (as understood by the combination of references, e.g., Figure 3 of Khan).
The combination of Khan and Deptford fails to teach:
wherein the food tolerance score comprises a numerical score associated with indicators based on a tolerability of the food item and its negative impacts.
However, Nag teaches a user mobile application (Figure 4) which displays a numerical score to meals based on preferences and personalized information, as explained below:
“Users also do not know quantitatively how their choices are affecting their health, which is why we have developed a ranking algorithm. The original concept of the algorithm is based on a ratio of healthy to unhealthy nutrients [7]. We assign a personalized health score (normalized from 1-100 with 100 being healthiest) to every physically local dish and food item based on the item nutritional facts and the user parameters (which includes their daily nutritional requirements and any dietary restrictions due to preexisting medical conditions such as diabetes)…
Different macro nutrients are assigned a weight for calculating the score which depends on the dietary restrictions placed on or the health goals of the user. For example, the score for a sugar rich meal is less for a diabetic person as the increased weight for the sugar reduces the overall score for the meal. Similarly, protein rich food items attain a higher score if the person’s goal is to gain muscle.
This score evaluates the items in a much more relevant manner for consumers to make their dietary choices compared to raw nutrition facts [7]. There are standardized algorithms available for measuring the nutrient density in the food items but none have been used in any consumer applications or incorporate the personal context of the user. We are incorporating the expert dietary recommendations of the various health professional society guidelines such as the American Heart Association and the American Diabetes Association [1] [8]. For example, in the case of diabetes, sugars is not recommended in the diet, hence the weighting factor was tuned by an expert dietitian to reflect this fact as shown in Table 1.” §3.3
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to calculate and display numerical scores corresponding to each of the meals within Khan’s user interface (Figure 6), as taught by Nag, for the advantage of simplifying a user’s meal selection to a single overall score instead of requiring a user to evaluate based on a list of nutritional facts.
Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
The combination of Khan, Deptford and Nag as cited above teaches:
wherein the food tolerance score comprises a numerical score (e.g., illustrated in Nag’s Figure 4, incorporated into Khan’s Figure 6) associated with indicators based on a tolerability of the food item (based on health goals or dietary restrictions, Nag §3.3) and its negative impacts (e.g., negative impact of sugar to a diabetic user or negative impact to muscle gain, Nag §3.3).
For claim 2, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Deptford further teaches:
calculate a conicity index as a function of the statistical makeup data (how seasons coincide with price and availability, “Identifying local foods and seasons”, pg. 3); and generate the alimentary data as a function of the micronutrient band and the conicity index (Linear programming calculations, pg. 7).
For claim 3, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Deptford further teaches:
the alimentary data comprises a nutrition deficiency of a demographic category of the user (“Change the amounts of energy, protein and micronutrients from the 1st to the 99th percentile of specifications for any given individual or collectively for a group of individuals”, The effect of changing underlying parameters, pg. 12).
For claim 4, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Deptford further teaches:
determine a plurality of phenotype clusters (e.g., by using age, body weight and physical activity, see “Selecting individuals and standard families”, pg. 5) within the geofence as a function of the alimentary data (“nutrient sufficiency can be modelled for a wide range of theoretical interventions for individuals, families or households”, pg. 12).
For claim 5, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Khan further teaches:
the user biological marker comprises a plurality of user body measurements (Figure 3 and as discussed in the rejection of claim 1).
For claim 6, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Khan further teaches:
the body measurements include at least a genetic body measurement (e.g., “liver mal function”, Figure 3).
For claim 7, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Deptford further teaches:
the statistical makeup data comprises a demographic category (food preferences of a demographic group based on location, “Data on the foods consumed by people in the locality of an assessment are collected in the field”, pg. 3 of Deptford).
For claim 8, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Deptford further teaches:
determine a plurality of phenotype clusters (e.g., gender, age, weight, physical activity, family or household, pg. 5) as a function of the micronutrient band (user modifiable, pg. 6; default micronutrient values as set by the WHO and FAO based on phenotype clusters, see “Selecting individuals and standard families”, pg. 5).
For claim 9, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Khan further teaches:
generate a menu as a function of the alimentary data (Figure 3); and
the menu options as a function of the food tolerance instruction set and the menu (as understood by the combination of the references).
For claim 10, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Khan further teaches:
generate, using a food analysis module of the processor, a food tolerance instruction set as a function of the food tolerance score (the portion of the processor which performs adaptation actions, e.g., increasing/decreasing portion size, Figure 4);
generate a food tolerance instruction set as a function of the food tolerance score (see rejection of claim 1); and
generate, using a menu generator module of the processor, a plurality of menu options as a function of the food tolerance instruction set and the alimentary data (Figures 2 and 3).
For claim 11, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 and Khan further teaches:
display the plurality of menu options using a graphical user interface (Figures 2-4 and 6).
For claim 13, Khan teaches a method of identifying compatible meal options (Abstract), the method comprising:
receiving a user biological marker, wherein the user biological marker comprises physiological data of a user (user data, e.g., diabetes or garlic allergy, §4.1 and Figure 3);
determining a food tolerance score as a function of the user biological marker, wherein the food tolerance score relates to a user ability to tolerate a food item (diet requirements, e.g., low sugar content due to diabetes, §4.1);
generating a food tolerance instruction set as a function of the food tolerance score (adaptation actions, e.g., increasing/decreasing portion size, Figure 4);
Khan fails to distinctly disclose:
receiving a geofence, wherein the geofence comprises a predetermined geographic area selected by the user;
identifying statistical makeup data as a function of the geofence;
generating a micronutrient band as a function of the statistical makeup data;
generating alimentary data as function of the micronutrient band, wherein the alimentary data comprises a recommended nutrient intake; and
identifying one or more meal options as a function of the food tolerance instruction set and the alimentary data.
instruction set and the alimentary data.
It is noted that Khan teaches modifying the menu generated for each user (e.g., Figure 3) by using rules (Figures 4 and 5). A rule is acquired when a modification action is performed and a corresponding user explanation is provided for why the action is necessary (pg. 168).
However, Deptford teaches a method of calculating the lowest cost of meeting recommended intakes of energy and nutrients from local foods (Abstract).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Khan’s system via rules such that affordable local foods are recommended over non-local foods in order to select the lease expensive combination of local foods that meets specifications for energy, macronutrients and micronutrients (Abstract) while considering local dietary habits (Implementation, pg. 2).
The combination of Khan and Deptford as defined above teaches:
receiving a geofence, wherein the geofence comprises a predetermined geographic area selected by the user (“an area in which people have a similar diet, such as a province or district, an urban or peri-urban area, an agro-ecological zone or a livelihood zone, especially when a household economy approach has recently been done”, pg. 3 of Deptford);
identifying statistical makeup data as a function of the geofence (“Data on the foods consumed by people in the locality of an assessment are collected in the field…Each food is then identified in the food database in the software by selecting either the example that is geographically closest to the assessment site or the generic CotD food…The list of foods can then be printed by the software and used to record current and retrospective prices in all seasons during a market survey”, pg. 3 of Deptford);
generating a micronutrient band as a function of the statistical makeup data (“[allowing] users to identify the micronutrients that most influence the cost of the diet… in order to minimize the risk of deficiency”, Vitamins and mineral specifications, pg. 6).
generating alimentary data as function of the micronutrient band, wherein the alimentary data comprises a recommended nutrient intake (preventing the software from exceeding specifications for micronutrients into toxic levels, Upper limits for specific nutrients, pg. 6); and
identifying one or more meal options as a function of the food tolerance instruction set and the alimentary data (as understood by the combination of references, e.g., Figure 3 of Khan).
The combination of Khan and Deptford fails to teach:
wherein the food tolerance score comprises a numerical score associated with indicators based on a tolerability of the food item and its negative impacts.
However, Nag teaches a user mobile application (Figure 4) which displays a numerical score to meals based on preferences and personalized information, as explained below:
“Users also do not know quantitatively how their choices are affecting their health, which is why we have developed a ranking algorithm. The original concept of the algorithm is based on a ratio of healthy to unhealthy nutrients [7]. We assign a personalized health score (normalized from 1-100 with 100 being healthiest) to every physically local dish and food item based on the item nutritional facts and the user parameters (which includes their daily nutritional requirements and any dietary restrictions due to preexisting medical conditions such as diabetes)…
Different macro nutrients are assigned a weight for calculating the score which depends on the dietary restrictions placed on or the health goals of the user. For example, the score for a sugar rich meal is less for a diabetic person as the increased weight for the sugar reduces the overall score for the meal. Similarly, protein rich food items attain a higher score if the person’s goal is to gain muscle.
This score evaluates the items in a much more relevant manner for consumers to make their dietary choices compared to raw nutrition facts [7]. There are standardized algorithms available for measuring the nutrient density in the food items but none have been used in any consumer applications or incorporate the personal context of the user. We are incorporating the expert dietary recommendations of the various health professional society guidelines such as the American Heart Association and the American Diabetes Association [1] [8]. For example, in the case of diabetes, sugars is not recommended in the diet, hence the weighting factor was tuned by an expert dietitian to reflect this fact as shown in Table 1.” §3.3
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to calculate and display numerical scores corresponding to each of the meals within Khan’s user interface (Figure 6), as taught by Nag, for the advantage of simplifying a user’s meal selection to a single overall score instead of requiring a user to evaluate based on a list of nutritional facts.
Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
The combination of Khan, Deptford and Nag as cited above teaches:
wherein the food tolerance score comprises a numerical score (e.g., illustrated in Nag’s Figure 4, incorporated into Khan’s Figure 6) associated with indicators based on a tolerability of the food item (based on health goals or dietary restrictions, Nag §3.3) and its negative impacts (e.g., negative impact of sugar to a diabetic user or negative impact to muscle gain, Nag §3.3).
For claim 14, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 13 and Deptford further teaches:
calculating a conicity index as a function of the statistical makeup data (how seasons coincide with price and availability, “Identifying local foods and seasons”, pg. 3); and generating the alimentary data as a function of the micronutrient band and the conicity index (Linear programming calculations, pg. 7).
For claim 15, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 13 and Deptford further teaches:
the alimentary data comprises a nutrition deficiency of a demographic category of the user (“Change the amounts of energy, protein and micronutrients from the 1st to the 99th percentile of specifications for any given individual or collectively for a group of individuals”, The effect of changing underlying parameters, pg. 12).
For claim 16, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 13 and Deptford further teaches:
determining a plurality of phenotype clusters (e.g., by using age, body weight and physical activity, see “Selecting individuals and standard families”, pg. 5) within the geofence as a function of the alimentary data (“nutrient sufficiency can be modelled for a wide range of theoretical interventions for individuals, families or households”, pg. 12).
For claim 17, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 13 and Khan further teaches:
the user biological marker comprises a plurality of user body measurements (Figure 3 and as discussed in the rejection of claim 13).
For claim 18, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 13 and Khan further teaches:
the body measurements include at least a genetic body measurement (e.g., “liver mal function”, Figure 3).
For claim 19, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 13 and Deptford further teaches:
the statistical makeup data comprises a demographic category (food preferences of a demographic group based on location, “Data on the foods consumed by people in the locality of an assessment are collected in the field”, pg. 3 of Deptford).
For claim 20, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 13 and Khan further teaches:
generate a menu as a function of the alimentary data (Figure 3); and
the menu options as a function of the food tolerance instruction set and the menu (as understood by the combination of the references).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan, Deptford, Nag and Holmes et al (US 2016/0180050).
For claim 12, the combination of Khan, Deptford and Nag as defined above teaches the limitations of claim 1 but fails to teach a machine-learning process as claimed.
However, Holmes teaches calculating a user effective age measurement (equivalent age of Figure 3, genetic age calculated based on relative risk, [0042]-[0057]) using a machine learning process (risk model, Figure 12 and [0037]-[0057]), wherein the machine learning process is trained with training data correlating a plurality of biological markers (predictor variables) to a plurality of effective age measurements (as understood by examination of Figures 3 and 12 and by [0042]-[0057]).
It is noted that Holmes’ invention is a system which “can provide health recommendations to a subject based on health data. Non-limiting examples of health recommendations include changes in lifestyle, physical activity, diet, medication, supplements, environmental factors, sun exposure, genetic testing, therapeutic intervention and screening for health conditions” ([0079]).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to implement Holmes’ health recommendations into the combination of Khan and Deptford as defined above so that health data can be considered into the lifestyle and dietary needs of a user.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL CALRISSIAN PUENTES whose telephone number is (571)270-5070. The examiner can normally be reached M-F 9-6:30 (flex).
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/DANIEL C PUENTES/Primary Examiner, Art Unit 2849