DETAILED ACTION
1. Claims 1-20 have been presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
PRIORITY
3. Acknowledgment is made of applicant's claim for priority to provisional application 63/253,214 filed on 10/07/2021.
Acknowledgment is made that this application is a continuation of application 17/691,662 filed 03/10/2022.
Response to Arguments
4. Applicant's arguments filed 12/19/25 have been fully considered but they are not persuasive.
i) Following Applicants arguments and amendments the previously presented 101 rejection is MAINTAINED. The Examiner notes that the newly amended “wherein measuring includes measuring physical and chemical characteristics” represents mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application. Therefore the 101 rejection is MAINTAINED.
ii) Following Applicants amendments and arguments the prior art rejection is MAINTAINED. Applicants argue that the prior art Protz “paragraph 33 does not describe quadruplets "of the form: first food product, second food product, taster, question." The pairs provided to users are pairs of "flavor descriptors" not food products (see paragraph 32).” The Examiner notes that the “flavor descriptor” represents elements of a food products and see also paragraph 46 of Protz, “The filtering means 25 may use established machine learning techniques to determine rules for a particular user and/or a particular foodstuff or type of foodstuff, which can be fed back 32 to the selection means 21 to adapt the selection of the pairs of descriptors in such a way as to refine the responses of the particular user and/or a particular foodstuff or type of foodstuff for greater accuracy.” Therefore the prior art rejection is MAINTAINED.
iii) Applicants argue that Protz does not teach the newly amended “wherein measuring includes measuring physical and chemical characteristics.” However Protz teaches in “[0005] Techniques analogous to colorimetry may also be used in the quantification of the human perception of touch, taste and smell. As with colorimetry, the human perception of the flavor and aroma of a substance is the perception of the interaction of taste receptors with physical or chemical quantities such as the molecular composition of the substance, which can be measured using chemistry or mass-spectrometry techniques, for example.” and in “[0008] In order to quantify the perception of the flavor of a substance accurately, it would in theory be possible, as mentioned above, to isolate the individual flavor-determining constituents of the substance and analyze (for example using chemical or biochemical techniques) the perceived flavor characteristics of each constituent and each combination of constituents, for different types of taster.”) Therefore the prior art rejection is MAINTAINED.
iv) Applicants argue that Protz does not teach “estimation” but rather “references flavor profiles generated by tasters.” Applicants argue that “Accordingly, there is no "estimation" involved, i.e., the flavor notes are explicitly received from the trained tasters and are not estimated. Protz therefore does not disclose "outputting estimated values…” However as noted in the previous office action Protz recites in at least “[0043] Normalizing means 26 is configured to normalize the selection results and to map them into a flavor profile space, such as the example depicted in FIG. 2, in database 8. In a traditional manual flavor evaluation environment, where the flavor notes are evaluated by individual trained tasters, a scaling of each flavor note can be carried out independently. This means that an additional flavor note can be added to the product and, if all others are left unchanged, this flavor note could be measured and “added” to the flavor profile.” The Examiner notes the recitation of both “a scaling of each flavor note can be carried out independently” and “this flavor note could be measured and “added” to the flavor profile” which both represent an estimation. Therefore the prior art rejection is MAINTAINED.
v) Applicants argue that Protz does not teach “estimating a graph associated with the similarities of each of the pluralities of food products, tasters and questions.” The graphical description of the flavor profile is seen in at least Fig. 2 showing the food product, chocolate, the tasters and their answers in graphical form. This concept is also seen in at least “[0035] The user-selections corresponding to the flavor descriptor pairs are adjusted by normalizing processor means 26 to map the selections into a multi-dimensional flavor descriptor intensity space which may be implemented as a suitable data structure in database 8. The normalizing means 26 weights each flavor descriptor in order to normalize the intensity values with respect to the other descriptor intensity scores. The normalizing process will be described in more detail below” as well as at least the explanation in [0049] of a “machine learning implemented in the filtering means 25 may, for example, include learning the different perception profiles of different tasters or different taster populations, and the flavor intensity profiles may be automatically adjusted using the learned rules. If the same product is tasted by two different groups, for example, using the same or similar descriptors, the filtering means 25 can be configured to automatically learn the differences in intensity scores calculated for the various descriptors from the two different flavor quantizations. These learned differences can then be used by the filtering means 25 to weight the descriptor intensity values higher or lower for a particular population, accordingly.” Therefore the prior art rejection is MAINTAINED.
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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
i) In view of Step 1 of the analysis, claim(s) 1, 9, 14, and 17 are directed to a statutory category as a process, which each represent a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
ii) In view of Step 2A, Prong One, claims 1, 9, 14, and 17 recite the abstract idea of evaluating the flavors of food using taste testers and questions which constitutes an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper as well as and alternatively as Methods of Organizing Human Activity.
As to claim 1, and similarly recited in claims 9, 14, and 17, the limitation of "identifying a plurality of food products;”, “identifying a plurality of tasters;”, and “identifying a plurality of questions;” would be analogous to a person choosing food to be evaluated, people to evaluate it, and then providing questions to measure the evaluation and thus fall under Mental Processes. In addition, the steps would constitute managing/evaluating the behavior of an individual based on certain types of evaluations of food which would fall under Methods of Organizing Human Activity. (See MPEP 2106.04(a)(2)(II)(C)) Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper, as well as and alternatively as Methods of Organizing Human Activity. This would similarly apply to the recitation of “identifying a full set of triplets of the form: food product, taster, question; identifying a subset of triplets from the full set of triplets” as in claim 14 as well as “identifying a refined subset of quadruplets from the full set of quadruplets; and iteratively refining the subset of quadruplets” as recited in claim 17.
As to claim 1, and similarly recited in claims 9, 14, and 17, the limitation of “identifying a full set of quadruplets of the form: first food product, second food product, taster, question;” and “identifying a subset of quadruplets from the full set of quadruplets;” would be analogous to a person choosing food to be evaluated, people to evaluate it, and then providing questions to measure the evaluation and thus fall under Mental Processes. In addition, the steps would constitute managing/evaluating the behavior of an individual based on certain types of evaluations of food which would fall under Methods of Organizing Human Activity. (See MPEP 2106.04(a)(2)(II)(C)) Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper, as well as and alternatively as Methods of Organizing Human Activity. This would similarly apply to the recitation of “identifying a plurality of at least one of new food products, new tasters, or new questions; identifying an extended set of quadruplets of the form: first food product, second food product, taster, question, wherein the extended set of quadruplets includes the new food products, new tasters and new questions” as in claim 9, as well as “estimating a graph associated with the similarities of each of the pluralities of food products, tasters and questions;” as in claim 14.
Dependent claims 2-8, 10-13, 15-16, and 18-20 further narrow the abstract ideas, identified in the independent claims.
iii) In view of Step 2A, Prong Two, the judicial exception is not integrated into a practical application. The limitation in claim 1, and similarly recited in claims 9, 14, and 17 of “measuring a value associated with each quadruplet from the subset of quadruplets, wherein measuring includes measuring physical and chemical characteristics;” and and “outputting estimated values associated with every triplet in a full set of triplets of the form: food product, taster, question” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “measuring a value associated with each quadruplet from the subset of quadruplets, wherein measuring includes measuring physical and chemical characteristics;” and “outputting estimated values associated with every triplet in a full set of triplets of the form: food product, taster, question” in claims 1, 9, 14, and 17, alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-8, 10-13, 15-16, and 18-20 further narrow the abstract ideas, identified in the independent claims and do not introduce further additional elements for consideration beyond those addressed above.
iv) In view of Step 2B, claims 1, 9, 14, and 17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation in claim 1, and similarly recited in claims 9, 14, and 17 of “measuring a value associated with each quadruplet from the subset of quadruplets, wherein measuring includes measuring physical and chemical characteristics;” and “outputting estimated values associated with every triplet in a full set of triplets of the form: food product, taster, question” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “measuring a value associated with each quadruplet from the subset of quadruplets, wherein measuring includes measuring physical and chemical characteristics;” and “outputting estimated values associated with every triplet in a full set of triplets of the form: food product, taster, question” in claims 1, 9, 14, and 17, alternatively can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2-8 further define the types of food, evaluation, and tasters of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or method of organized human activity.
Dependent claims 10-13 further define the types of food and evaluation of respective claim 9 which merely narrows the abstract idea identified as a mental process and/or method of organized human activity.
Dependent claims 15-16 further define the types of evaluation of respective claim 14 which merely narrows the abstract idea identified as a mental process and/or method of organized human activity.
Dependent claims 18-20 further define the types of food, evaluation, and tasters of respective claim 17 which merely narrows the abstract idea identified as a mental process and/or method of organized human activity.
v) Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
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.
6. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Protz U.S. Patent Publication No. 20210073836.
Regarding Claim 1: The reference discloses A method comprising:
identifying a plurality of food products; (“[0028] FIG. 2 shows a graphical representation of a flavor descriptor space 4 for representing the flavor profile of a foodstuff.”)
identifying a plurality of tasters; (“[0008] In order to quantify the perception of the flavor of a substance accurately, it would in theory be possible, as mentioned above, to isolate the individual flavor-determining constituents of the substance and analyze (for example using chemical or biochemical techniques) the perceived flavor characteristics of each constituent and each combination of constituents, for different types of taster. However, this is likely to be impracticable. Instead, it is usual to employ panels of experienced human tasters, and to train the tasters to ensure a consistent, repeatable evaluation of the flavor or aroma concerned.”)
identifying a plurality of questions; (“[0013] By capturing many answers to pairwise comparison questions, therefore, it is possible to achieve a similar quality of perception variable (descriptor) quantification as achieved by expert colorimetrists or tasting panels, but without requiring the colorimetrists/tasters to be trained, and without the need for large-scale data processing capacity to compute results.”)
identifying a full set of quadruplets of the form: first food product, second food product, taster, question; (“[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
identifying a subset of quadruplets from the full set of quadruplets; (“[0034] Filtering means 25 may be provided for filtering out anomalous user-selections, or for excluding a particular user's selections from the selection data which is to be mapped into the flavor descriptor space in database 8, or for weighting user selections according to some other criterion. Optionally, an output 32 from the filtering means 25 may be used as an adaptive control input to the selection means 21 such that the selection of flavor descriptor pairs may be carried out adaptively, for example in order to repeat interrogation of descriptor pairs for which an anomalous user-selection has been detected by the filtering means 25. As will be described below, filtering means 25 may be implemented as a machine learning function and/or knowledge base of previous user selection patterns.”)
measuring a value associated with each quadruplet from the subset of quadruplets, (“[0043] Normalizing means 26 is configured to normalize the selection results and to map them into a flavor profile space, such as the example depicted in FIG. 2, in database 8. In a traditional manual flavor evaluation environment, where the flavor notes are evaluated by individual trained tasters, a scaling of each flavor note can be carried out independently. This means that an additional flavor note can be added to the product and, if all others are left unchanged, this flavor note could be measured and “added” to the flavor profile.”)
wherein measuring includes measuring physical and chemical characteristics; and (“[0005] Techniques analogous to colorimetry may also be used in the quantification of the human perception of touch, taste and smell. As with colorimetry, the human perception of the flavor and aroma of a substance is the perception of the interaction of taste receptors with physical or chemical quantities such as the molecular composition of the substance, which can be measured using chemistry or mass-spectrometry techniques, for example.” “[0008] In order to quantify the perception of the flavor of a substance accurately, it would in theory be possible, as mentioned above, to isolate the individual flavor-determining constituents of the substance and analyze (for example using chemical or biochemical techniques) the perceived flavor characteristics of each constituent and each combination of constituents, for different types of taster.”)
outputting estimated values associated with every triplet in a full set of triplets of the form: food product, taster, question. (“[0043] Normalizing means 26 is configured to normalize the selection results and to map them into a flavor profile space, such as the example depicted in FIG. 2, in database 8. In a traditional manual flavor evaluation environment, where the flavor notes are evaluated by individual trained tasters, a scaling of each flavor note can be carried out independently. This means that an additional flavor note can be added to the product and, if all others are left unchanged, this flavor note could be measured and “added” to the flavor profile.”)
Regarding Claim 2: The reference discloses The method of claim 1, wherein the second food product is identical to the first food product. (“[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
Regarding Claim 3: The reference discloses The method of claim 1, further comprising identifying a similarity function associated with the plurality of food products. (“[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
Regarding Claim 4: The reference discloses The method of claim 1, further comprising identifying a similarity function associated with the plurality of tasters. (“[0049] The method and system described above is designed to provide an accurate and consistent quantification of the flavor of a product. The normalized quantization of flavor descriptor information permits the generation of flavor profile information which is scaled rather than the declarative information of traditional tasting panel methods. This enables tastings carried out by different taster populations to be compared numerically with each other, so that flavor descriptor profiles from the different populations can be represented in the same descriptor space. The machine learning implemented in the filtering means 25 may, for example, include learning the different perception profiles of different tasters or different taster populations, and the flavor intensity profiles may be automatically adjusted using the learned rules. If the same product is tasted by two different groups, for example, using the same or similar descriptors, the filtering means 25 can be configured to automatically learn the differences in intensity scores calculated for the various descriptors from the two different flavor quantizations.”)
Regarding Claim 5: The reference discloses The method of claim 1, further comprising identifying a similarity function associated with the plurality of questions. (“[0049] The method and system described above is designed to provide an accurate and consistent quantification of the flavor of a product. The normalized quantization of flavor descriptor information permits the generation of flavor profile information which is scaled rather than the declarative information of traditional tasting panel methods. This enables tastings carried out by different taster populations to be compared numerically with each other, so that flavor descriptor profiles from the different populations can be represented in the same descriptor space. The machine learning implemented in the filtering means 25 may, for example, include learning the different perception profiles of different tasters or different taster populations, and the flavor intensity profiles may be automatically adjusted using the learned rules. If the same product is tasted by two different groups, for example, using the same or similar descriptors, the filtering means 25 can be configured to automatically learn the differences in intensity scores calculated for the various descriptors from the two different flavor quantizations.”)
Regarding Claim 6: The reference discloses The method of claim 1, wherein the plurality of food products include single ingredients or multiple ingredients. ([0040] “For example, a when the stimulus is the taste of a strawberry yogurt, the filtering means 25 may relatively quickly move to a high-granularity using strawberry flavor descriptors (of which there are many).”)
Regarding Claim 7: The reference discloses The method of claim 1, wherein the plurality of tasters include a plurality of individual people tasting the plurality of food products. (“[0049] The method and system described above is designed to provide an accurate and consistent quantification of the flavor of a product. The normalized quantization of flavor descriptor information permits the generation of flavor profile information which is scaled rather than the declarative information of traditional tasting panel methods. This enables tastings carried out by different taster populations to be compared numerically with each other, so that flavor descriptor profiles from the different populations can be represented in the same descriptor space. The machine learning implemented in the filtering means 25 may, for example, include learning the different perception profiles of different tasters or different taster populations, and the flavor intensity profiles may be automatically adjusted using the learned rules. If the same product is tasted by two different groups, for example, using the same or similar descriptors, the filtering means 25 can be configured to automatically learn the differences in intensity scores calculated for the various descriptors from the two different flavor quantizations.”)
Regarding Claim 8: The reference discloses The method of claim 1, wherein the plurality of questions include topics such as savor, smell, texture, or mouthfeel. (“[0005] Techniques analogous to colorimetry may also be used in the quantification of the human perception of touch, taste and smell.” [0008] “Tasters are trained on a scale of intensities for flavor, texture and aroma.”)
Regarding Claim 9: The reference discloses A method comprising:
identifying a plurality of food products; (“[0028] FIG. 2 shows a graphical representation of a flavor descriptor space 4 for representing the flavor profile of a foodstuff.”)
identifying a plurality of tasters; (“[0008] In order to quantify the perception of the flavor of a substance accurately, it would in theory be possible, as mentioned above, to isolate the individual flavor-determining constituents of the substance and analyze (for example using chemical or biochemical techniques) the perceived flavor characteristics of each constituent and each combination of constituents, for different types of taster. However, this is likely to be impracticable. Instead, it is usual to employ panels of experienced human tasters, and to train the tasters to ensure a consistent, repeatable evaluation of the flavor or aroma concerned.”)
identifying a plurality of questions; (“[0013] By capturing many answers to pairwise comparison questions, therefore, it is possible to achieve a similar quality of perception variable (descriptor) quantification as achieved by expert colorimetrists or tasting panels, but without requiring the colorimetrists/tasters to be trained, and without the need for large-scale data processing capacity to compute results.”)
identifying a full set of quadruplets of the form: first food product, second food product, taster, question; (“[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
identifying a subset of quadruplets from the full set of quadruplets; (“[0034] Filtering means 25 may be provided for filtering out anomalous user-selections, or for excluding a particular user's selections from the selection data which is to be mapped into the flavor descriptor space in database 8, or for weighting user selections according to some other criterion. Optionally, an output 32 from the filtering means 25 may be used as an adaptive control input to the selection means 21 such that the selection of flavor descriptor pairs may be carried out adaptively, for example in order to repeat interrogation of descriptor pairs for which an anomalous user-selection has been detected by the filtering means 25. As will be described below, filtering means 25 may be implemented as a machine learning function and/or knowledge base of previous user selection patterns.”)
measuring a value associated with each quadruplets from the subset of quadruplets, (“[0043] Normalizing means 26 is configured to normalize the selection results and to map them into a flavor profile space, such as the example depicted in FIG. 2, in database 8. In a traditional manual flavor evaluation environment, where the flavor notes are evaluated by individual trained tasters, a scaling of each flavor note can be carried out independently. This means that an additional flavor note can be added to the product and, if all others are left unchanged, this flavor note could be measured and “added” to the flavor profile.”)
wherein measuring includes measuring physical and chemical characteristics; and (“[0005] Techniques analogous to colorimetry may also be used in the quantification of the human perception of touch, taste and smell. As with colorimetry, the human perception of the flavor and aroma of a substance is the perception of the interaction of taste receptors with physical or chemical quantities such as the molecular composition of the substance, which can be measured using chemistry or mass-spectrometry techniques, for example.” “[0008] In order to quantify the perception of the flavor of a substance accurately, it would in theory be possible, as mentioned above, to isolate the individual flavor-determining constituents of the substance and analyze (for example using chemical or biochemical techniques) the perceived flavor characteristics of each constituent and each combination of constituents, for different types of taster.”)
identifying a plurality of at least one of new food products, new tasters, or new questions; (“[0039] As mentioned above, the descriptor pairs may advantageously be generated dynamically, in real time. This so-called ‘smart’ generation of the descriptor pairs may be adapted to eliminate or reduce the occurrence of pairs which are judged unlikely to provide significant additional characterization data of the stimulus (pairs to which all users consistently in the same way, for example, or pairs which relate to a region of the descriptor space where it has been assessed that an individual user has a poor record of consistency). Certain pairs may lie in a region of the descriptor space where there is already sufficient data density, so these may be partially or completely suppressed. Or the opposite may be the case—a region of particular interest in the descriptor space may be sparsely populated, in which case more pairs could be generated for this region. In a situation where users are randomly located around the world, it may be that certain regions are under or over-represented, in which case the number and type of pairs may be dynamically adjusted in real time response. The age of the data points in the descriptor space may also be taken into account. For example, if the data in a particular region are unusually old, then the number of new descriptor pairs may be increase to replace or supplement the existing data. In some applications, users' perceptions may change with time, so it may be important to update the descriptor space.”)
identifying an extended set of quadruplets of the form: first food product, second food product, taster, question, wherein the extended set of quadruplets includes the new food products, new tasters and new questions; and (“[0039] As mentioned above, the descriptor pairs may advantageously be generated dynamically, in real time. This so-called ‘smart’ generation of the descriptor pairs may be adapted to eliminate or reduce the occurrence of pairs which are judged unlikely to provide significant additional characterization data of the stimulus (pairs to which all users consistently in the same way, for example, or pairs which relate to a region of the descriptor space where it has been assessed that an individual user has a poor record of consistency). Certain pairs may lie in a region of the descriptor space where there is already sufficient data density, so these may be partially or completely suppressed. Or the opposite may be the case—a region of particular interest in the descriptor space may be sparsely populated, in which case more pairs could be generated for this region. In a situation where users are randomly located around the world, it may be that certain regions are under or over-represented, in which case the number and type of pairs may be dynamically adjusted in real time response. The age of the data points in the descriptor space may also be taken into account. For example, if the data in a particular region are unusually old, then the number of new descriptor pairs may be increase to replace or supplement the existing data. In some applications, users' perceptions may change with time, so it may be important to update the descriptor space.”)
outputting estimated values associated with every quadruplet from the extended set of quadruplets of the form: food product, taster, question. (“[0034] Filtering means 25 may be provided for filtering out anomalous user-selections, or for excluding a particular user's selections from the selection data which is to be mapped into the flavor descriptor space in database 8, or for weighting user selections according to some other criterion. Optionally, an output 32 from the filtering means 25 may be used as an adaptive control input to the selection means 21 such that the selection of flavor descriptor pairs may be carried out adaptively, for example in order to repeat interrogation of descriptor pairs for which an anomalous user-selection has been detected by the filtering means 25. As will be described below, filtering means 25 may be implemented as a machine learning function and/or knowledge base of previous user selection patterns.”)
Regarding Claim 10: The reference discloses The method of claim 9, further comprising identifying a similarity function associated with the plurality of food products. (“[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
Regarding Claim 11: The reference discloses The method of claim 9, further comprising identifying a similarity function associated with the plurality of tasters. (“[0049] The method and system described above is designed to provide an accurate and consistent quantification of the flavor of a product. The normalized quantization of flavor descriptor information permits the generation of flavor profile information which is scaled rather than the declarative information of traditional tasting panel methods. This enables tastings carried out by different taster populations to be compared numerically with each other, so that flavor descriptor profiles from the different populations can be represented in the same descriptor space. The machine learning implemented in the filtering means 25 may, for example, include learning the different perception profiles of different tasters or different taster populations, and the flavor intensity profiles may be automatically adjusted using the learned rules. If the same product is tasted by two different groups, for example, using the same or similar descriptors, the filtering means 25 can be configured to automatically learn the differences in intensity scores calculated for the various descriptors from the two different flavor quantizations.”)
Regarding Claim 12: The reference discloses The method of claim 9, further comprising identifying a similarity function associated with the plurality of questions. (“[0049] The method and system described above is designed to provide an accurate and consistent quantification of the flavor of a product. The normalized quantization of flavor descriptor information permits the generation of flavor profile information which is scaled rather than the declarative information of traditional tasting panel methods. This enables tastings carried out by different taster populations to be compared numerically with each other, so that flavor descriptor profiles from the different populations can be represented in the same descriptor space. The machine learning implemented in the filtering means 25 may, for example, include learning the different perception profiles of different tasters or different taster populations, and the flavor intensity profiles may be automatically adjusted using the learned rules. If the same product is tasted by two different groups, for example, using the same or similar descriptors, the filtering means 25 can be configured to automatically learn the differences in intensity scores calculated for the various descriptors from the two different flavor quantizations.”)
Regarding Claim 13: The reference discloses The method of claim 9, wherein the first food product is identical to the second food product. (“[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
Regarding Claim 14: The reference discloses A method comprising:
identifying a plurality of food products; (“[0028] FIG. 2 shows a graphical representation of a flavor descriptor space 4 for representing the flavor profile of a foodstuff.”)
identifying a plurality of tasters; (“[0008] In order to quantify the perception of the flavor of a substance accurately, it would in theory be possible, as mentioned above, to isolate the individual flavor-determining constituents of the substance and analyze (for example using chemical or biochemical techniques) the perceived flavor characteristics of each constituent and each combination of constituents, for different types of taster. However, this is likely to be impracticable. Instead, it is usual to employ panels of experienced human tasters, and to train the tasters to ensure a consistent, repeatable evaluation of the flavor or aroma concerned.”)
identifying a plurality of questions; (“[0013] By capturing many answers to pairwise comparison questions, therefore, it is possible to achieve a similar quality of perception variable (descriptor) quantification as achieved by expert colorimetrists or tasting panels, but without requiring the colorimetrists/tasters to be trained, and without the need for large-scale data processing capacity to compute results.”)
identifying a full set of triplets of the form: food product, taster, question; (“[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
identifying a subset of triplets from the full set of triplets; (“[0034] Filtering means 25 may be provided for filtering out anomalous user-selections, or for excluding a particular user's selections from the selection data which is to be mapped into the flavor descriptor space in database 8, or for weighting user selections according to some other criterion. Optionally, an output 32 from the filtering means 25 may be used as an adaptive control input to the selection means 21 such that the selection of flavor descriptor pairs may be carried out adaptively, for example in order to repeat interrogation of descriptor pairs for which an anomalous user-selection has been detected by the filtering means 25. As will be described below, filtering means 25 may be implemented as a machine learning function and/or knowledge base of previous user selection patterns.”)
measuring a value associated with each triplet from the subset of triplets, (“[0043] Normalizing means 26 is configured to normalize the selection results and to map them into a flavor profile space, such as the example depicted in FIG. 2, in database 8. In a traditional manual flavor evaluation environment, where the flavor notes are evaluated by individual trained tasters, a scaling of each flavor note can be carried out independently. This means that an additional flavor note can be added to the product and, if all others are left unchanged, this flavor note could be measured and “added” to the flavor profile.”)
wherein measuring includes measuring physical and chemical characteristics; and (“[0005] Techniques analogous to colorimetry may also be used in the quantification of the human perception of touch, taste and smell. As with colorimetry, the human perception of the flavor and aroma of a substance is the perception of the interaction of taste receptors with physical or chemical quantities such as the molecular composition of the substance, which can be measured using chemistry or mass-spectrometry techniques, for example.” “[0008] In order to quantify the perception of the flavor of a substance accurately, it would in theory be possible, as mentioned above, to isolate the individual flavor-determining constituents of the substance and analyze (for example using chemical or biochemical techniques) the perceived flavor characteristics of each constituent and each combination of constituents, for different types of taster.”)
estimating a graph associated with the similarities of each of the pluralities of food products, tasters and questions; and (“[0028] FIG. 2 shows a graphical representation of a flavor descriptor space 4 for representing the flavor profile of a foodstuff. In this example, thirteen flavor descriptors are depicted on thirteen radial axes, each with an intensity scale of 0-100%. The thirteen descriptors have been pre-selected in this example as being suitable for characterizing dark chocolate products. Two different dark chocolate products are characterized by the dotted and dashed lines 5 and 6.”)
outputting estimated values associated with every triplet in the full set of triplets. (“[0034] Filtering means 25 may be provided for filtering out anomalous user-selections, or for excluding a particular user's selections from the selection data which is to be mapped into the flavor descriptor space in database 8, or for weighting user selections according to some other criterion. Optionally, an output 32 from the filtering means 25 may be used as an adaptive control input to the selection means 21 such that the selection of flavor descriptor pairs may be carried out adaptively, for example in order to repeat interrogation of descriptor pairs for which an anomalous user-selection has been detected by the filtering means 25. As will be described below, filtering means 25 may be implemented as a machine learning function and/or knowledge base of previous user selection patterns.”)
Regarding Claim 15: The reference discloses The method of claim 14, further comprising identifying a parametric family of similarity functions. (“[0001] The present invention relates to computer-implemented systems and methods for empirically measuring a person's perception of a predetermined stimulus. In particular, it relates to the rendering in multivariate parameter space the perception of the stimulus, based on a predetermined set of variables which characterize the stimulus.” “[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
Regarding Claim 16: The reference discloses The method of claim 14, further comprising estimating the parameters of the family of similarly functions from which the graph is estimated. (“[0043] Normalizing means 26 is configured to normalize the selection results and to map them into a flavor profile space, such as the example depicted in FIG. 2, in database 8. In a traditional manual flavor evaluation environment, where the flavor notes are evaluated by individual trained tasters, a scaling of each flavor note can be carried out independently. This means that an additional flavor note can be added to the product and, if all others are left unchanged, this flavor note could be measured and “added” to the flavor profile.” See also “[0046] The filtering means 25 may advantageously comprise means for detecting illogical or inconsistent selections of a user. For example, it may detect logical inconsistencies in responses such as (fruity*/nutty) (nutty*/spicy) (fruity/spicy*), where the asterisks indicate the user's selections for these three descriptor pairs. This may be an indication that the particular user finds it difficult to discern flavor notes, or certain types of flavor notes. When such an inconsistency is detected by the filtering means 25, the pairs or sets of pairs concerned can then be automatically re-prompted, by sending instructions via adaptive control communications link 32, until consistency is found. The filtering means 25 may use established machine learning techniques to determine rules for a particular user and/or a particular foodstuff or type of foodstuff, which can be fed back 32 to the selection means 21 to adapt the selection of the pairs of descriptors in such a way as to refine the responses of the particular user and/or a particular foodstuff or type of foodstuff for greater accuracy.”)
Regarding Claim 17: The reference discloses A method comprising:
identifying a plurality of food products; (“[0028] FIG. 2 shows a graphical representation of a flavor descriptor space 4 for representing the flavor profile of a foodstuff.”)
identifying a plurality of tasters; (“[0008] In order to quantify the perception of the flavor of a substance accurately, it would in theory be possible, as mentioned above, to isolate the individual flavor-determining constituents of the substance and analyze (for example using chemical or biochemical techniques) the perceived flavor characteristics of each constituent and each combination of constituents, for different types of taster. However, this is likely to be impracticable. Instead, it is usual to employ panels of experienced human tasters, and to train the tasters to ensure a consistent, repeatable evaluation of the flavor or aroma concerned.”)
identifying a plurality of questions; (“[0013] By capturing many answers to pairwise comparison questions, therefore, it is possible to achieve a similar quality of perception variable (descriptor) quantification as achieved by expert colorimetrists or tasting panels, but without requiring the colorimetrists/tasters to be trained, and without the need for large-scale data processing capacity to compute results.”)
identifying a full set of quadruplets of the form: first food product, second food product, taster, question; (“[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
identifying an initial subset of quadruplets from the full set of quadruplets; (“[0034] Filtering means 25 may be provided for filtering out anomalous user-selections, or for excluding a particular user's selections from the selection data which is to be mapped into the flavor descriptor space in database 8, or for weighting user selections according to some other criterion. Optionally, an output 32 from the filtering means 25 may be used as an adaptive control input to the selection means 21 such that the selection of flavor descriptor pairs may be carried out adaptively, for example in order to repeat interrogation of descriptor pairs for which an anomalous user-selection has been detected by the filtering means 25. As will be described below, filtering means 25 may be implemented as a machine learning function and/or knowledge base of previous user selection patterns.”)
measuring a value associated with each quadruplets from the initial subset of quadruplets, (“[0043] Normalizing means 26 is configured to normalize the selection results and to map them into a flavor profile space, such as the example depicted in FIG. 2, in database 8. In a traditional manual flavor evaluation environment, where the flavor notes are evaluated by individual trained tasters, a scaling of each flavor note can be carried out independently. This means that an additional flavor note can be added to the product and, if all others are left unchanged, this flavor note could be measured and “added” to the flavor profile.”)
wherein measuring includes measuring physical and chemical characteristics; and (“[0005] Techniques analogous to colorimetry may also be used in the quantification of the human perception of touch, taste and smell. As with colorimetry, the human perception of the flavor and aroma of a substance is the perception of the interaction of taste receptors with physical or chemical quantities such as the molecular composition of the substance, which can be measured using chemistry or mass-spectrometry techniques, for example.” “[0008] In order to quantify the perception of the flavor of a substance accurately, it would in theory be possible, as mentioned above, to isolate the individual flavor-determining constituents of the substance and analyze (for example using chemical or biochemical techniques) the perceived flavor characteristics of each constituent and each combination of constituents, for different types of taster.”)
outputting estimated values associated with every quadruplet in the full set of quadruplets of the form: food product, taster, question; (“[0034] Filtering means 25 may be provided for filtering out anomalous user-selections, or for excluding a particular user's selections from the selection data which is to be mapped into the flavor descriptor space in database 8, or for weighting user selections according to some other criterion. Optionally, an output 32 from the filtering means 25 may be used as an adaptive control input to the selection means 21 such that the selection of flavor descriptor pairs may be carried out adaptively, for example in order to repeat interrogation of descriptor pairs for which an anomalous user-selection has been detected by the filtering means 25. As will be described below, filtering means 25 may be implemented as a machine learning function and/or knowledge base of previous user selection patterns.”)
identifying a refined subset of quadruplets from the full set of quadruplets; (“[0043] Normalizing means 26 is configured to normalize the selection results and to map them into a flavor profile space, such as the example depicted in FIG. 2, in database 8. In a traditional manual flavor evaluation environment, where the flavor notes are evaluated by individual trained tasters, a scaling of each flavor note can be carried out independently. This means that an additional flavor note can be added to the product and, if all others are left unchanged, this flavor note could be measured and “added” to the flavor profile.” See also “[0046] The filtering means 25 may advantageously comprise means for detecting illogical or inconsistent selections of a user. For example, it may detect logical inconsistencies in responses such as (fruity*/nutty) (nutty*/spicy) (fruity/spicy*), where the asterisks indicate the user's selections for these three descriptor pairs. This may be an indication that the particular user finds it difficult to discern flavor notes, or certain types of flavor notes. When such an inconsistency is detected by the filtering means 25, the pairs or sets of pairs concerned can then be automatically re-prompted, by sending instructions via adaptive control communications link 32, until consistency is found. The filtering means 25 may use established machine learning techniques to determine rules for a particular user and/or a particular foodstuff or type of foodstuff, which can be fed back 32 to the selection means 21 to adapt the selection of the pairs of descriptors in such a way as to refine the responses of the particular user and/or a particular foodstuff or type of foodstuff for greater accuracy.”)
measuring a value associated with each quadruplet from the refined subset of quadruplets; and (“[0043] Normalizing means 26 is configured to normalize the selection results and to map them into a flavor profile space, such as the example depicted in FIG. 2, in database 8. In a traditional manual flavor evaluation environment, where the flavor notes are evaluated by individual trained tasters, a scaling of each flavor note can be carried out independently. This means that an additional flavor note can be added to the product and, if all others are left unchanged, this flavor note could be measured and “added” to the flavor profile.”)
iteratively refining the subset of quadruplets. ([0046] “The filtering means 25 may use established machine learning techniques to determine rules for a particular user and/or a particular foodstuff or type of foodstuff, which can be fed back 32 to the selection means 21 to adapt the selection of the pairs of descriptors in such a way as to refine the responses of the particular user and/or a particular foodstuff or type of foodstuff for greater accuracy.”)
Regarding Claim 18: The reference discloses The method of claim 17, further comprising identifying a similarity function associated with the plurality of food products. (“[0033] As described above, the pairs may be transmitted sequentially from the host 1 to the user device 2. Alternatively, the set of pairs for one product for one taster may all be transmitted together, or in batches of multiple pairs, to the user device 2. Similarly, the user device 2 may transmit the response selections to the receiving means 24 in real time, i.e. as the selections are made, or it may accumulate some or all of the responses before transmitting them all together or in batches of multiple responses to the receiving means 24.”)
Regarding Claim 19: The reference discloses The method of claim 17, further comprising identifying a similarity function associated with the plurality of tasters. (“[0049] The method and system described above is designed to provide an accurate and consistent quantification of the flavor of a product. The normalized quantization of flavor descriptor information permits the generation of flavor profile information which is scaled rather than the declarative information of traditional tasting panel methods. This enables tastings carried out by different taster populations to be compared numerically with each other, so that flavor descriptor profiles from the different populations can be represented in the same descriptor space. The machine learning implemented in the filtering means 25 may, for example, include learning the different perception profiles of different tasters or different taster populations, and the flavor intensity profiles may be automatically adjusted using the learned rules. If the same product is tasted by two different groups, for example, using the same or similar descriptors, the filtering means 25 can be configured to automatically learn the differences in intensity scores calculated for the various descriptors from the two different flavor quantizations.”)
Regarding Claim 20: The reference discloses The method of claim 17, further comprising identifying a similarity function associated with the plurality of questions. (“[0049] The method and system described above is designed to provide an accurate and consistent quantification of the flavor of a product. The normalized quantization of flavor descriptor information permits the generation of flavor profile information which is scaled rather than the declarative information of traditional tasting panel methods. This enables tastings carried out by different taster populations to be compared numerically with each other, so that flavor descriptor profiles from the different populations can be represented in the same descriptor space. The machine learning implemented in the filtering means 25 may, for example, include learning the different perception profiles of different tasters or different taster populations, and the flavor intensity profiles may be automatically adjusted using the learned rules. If the same product is tasted by two different groups, for example, using the same or similar descriptors, the filtering means 25 can be configured to automatically learn the differences in intensity scores calculated for the various descriptors from the two different flavor quantizations.”)
Conclusion
7. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
8. All Claims are rejected.
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) U.S. Patent Publication No. 20070122509
ii) Negri, Rossella, et al. "Taste perception and food choices." Journal of pediatric gastroenterology and nutrition 54.5 (2012): 624-629.
iii) Keast, Russell SJ, and Paul AS Breslin. "An overview of binary taste–taste interactions." Food quality and preference 14.2 (2003): 111-124.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2188