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 .
The following is a Final Office Action in response to communications received on 8/25/2025. Claims 1-10 are currently pending and have been examined. Claims 1-8 have been amended. Claims 9-10 have been added.
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.
Claim 9 is 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “similar” in claim 9 is a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Pages 8-9 of the specification provide examples of what data might be used for the determination of “similar” or “similarities” among a group of users, however the term itself is not limited by the specification by definition or by the claim as to the standard for ascertaining the requisite degree. Clarification is needed. For the purposes of compact prosecution, the term has been interpreted to be those users with overlapping values for the rate of purchase amounts or rates of purchase volumes.
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.
Step 1: The claims 1-6, 9 and 10 are a system, claim 7 is a method, and claim 8 is a computer readable medium. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claims (1, 7 and 8 with claim 1 taken as a representative claim) recite:
A health support system, comprising: a computer configured to:
acquire payment data of a user;
generate, using purchasing behavior determined from the payment data, a group including a plurality of members including the user;
acquire physical data of each of the plurality of members;
detect a change in a value of a predetermined data item in the physical data;
extract purchase history of a product or a service that changes the value of the predetermined data item from the payment data of the user regarding whom the change in the value of the predetermined data item has been detected;
specify, from the extracted purchase history up to a time traced back by a predetermined period from a time when the value of the predetermined data item was changed a product or a service that changes the value of the predetermined data item, by referring to a list in which products or services for changing values of predetermined data items are predefined for each of the predetermined data items;
generate recommendation information for recommending the
transmit a signal to user terminal devices of other members of the group to notify the other members of the group of the generated recommendation information.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for receiving transaction related data, health related data of a set of members, and generating recommendations for products based on a correlation of the data sets. The steps under its broadest reasonable interpretation specifically fall under sales and marketing activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
A health support system, comprising: a computer configured to: (claim 1)
A non-transitory computer readable medium storing a program for causing a computer to execute the following processing of: (claim 7)
transmit a signal to user terminal devices
The additional elements of A health support system, comprising: a computer configured to: (claim 1); A non-transitory computer readable medium storing a program for causing a computer to execute the following processing of: (claim 7) transmit a signal to user terminal devices; are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Dependent claims 2-9 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 7 and 8 without significantly more.
Claim 2 recites wherein the recommendation information generation unit generates the recommendation information including an amount of change in the value of the predetermined data item. The limitation merely further limits that abstract idea without reciting anything significantly more to integrate the judicial exception into a practical application.
Claim 3 recites wherein the recommendation information generation unit generates the recommendation information including a purchase frequency or a purchase volume by the member regarding whom the change in the value of the predetermined data item has been detected regarding the products specified by the extracted purchase history. The limitation merely further limits that abstract idea without reciting anything significantly more to integrate the judicial exception into a practical application.
Claim 4 recites wherein the recommendation information generation unit generates the recommendation information including a usage frequency by or a usage period of the member regarding whom the change in the value of the predetermined data item has been detected regarding the services specified by the extracted purchase history. The limitation merely further limits that abstract idea without reciting anything significantly more to integrate the judicial exception into a practical application.
Claim 5 recites wherein the recommendation information generation unit generates the recommendation information further including information for recommending products or services related to the products or the services specified by the extracted purchase history. The limitation merely further limits that abstract idea without reciting anything significantly more to integrate the judicial exception into a practical application.
Claim 6 recites wherein the member data acquisition unit further acquires data of an exercise history of each of the members, and the recommendation information generation unit generates the recommendation information for recommending, among the products or the services specified by the extracted purchase history, the product or the service selected based on the exercise history of the member regarding whom the change in the value of the predetermined data item has been detected. The limitation merely further limits that abstract idea without reciting anything significantly more to integrate the judicial exception into a practical application.
Claim 9 recites wherein the computer is configured to generate, using the payment data, the group of the plurality of members whose rates of purchase amounts or rates of purchase volumes for each category of products or services are similar to one another. The limitation merely further limits that abstract idea without reciting anything significantly more to integrate the judicial exception into a practical application.
Claim 10 recites wherein the computer is configured to generate, using the payment data, the group of the plurality of members whose purchase amounts or purchase volumes of specific products or services exceed a threshold. The limitation merely further limits that abstract idea without reciting anything significantly more to integrate the judicial exception into a practical application.
For these reasons the claims have been rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1, 2, 5, 6, 7, 8 are rejected under 35 U.S.C. 103 as being unpatentable over Harve (US 20180012283) in view of Kartoun (US 10971269).
Regarding claims 1, 7 and 8, Harve discloses:
A health support system comprising: a computer configured to: (claim1) (see Figure 1)
A health support method comprising: (claim 7)
A non-transitory computer readable medium storing a program for causing a computer to execute the following processing of: (claim 8) (see [0019])
acquire payment data of a user; ("[0104] transaction table 806 and [0106] A history table 814 maintains a history of transactions to which a user has been a party.)
generate, using purchasing behavior determined from the payment data, a group including a plurality of members including the user; ([0013] Further, other users that share a similar lifestyle may have a wealth of knowledge and a purchase history of products and services that could be leveraged to help a given user get relevant product and service recommendations. And see [0059-60]) Thus, the combination of user physical activity patterns and previous purchases of the user and/or other users sharing similar physical activity patterns may define a market segment and corresponding products or services relevant to that market segment. [0060] Similarly, a market segment of users may include bicyclists, and server 412 may provide them with bicycling related recommendations.
acquire physical data of each of the plurality of members; (And see [0059-60]) Thus, the combination of user physical activity patterns and previous purchases of the user and/or other users sharing similar physical activity patterns may define a market segment and corresponding products or services relevant to that market segment. [0060] Similarly, a market segment of users may include bicyclists, and server 412 may provide them with bicycling related recommendations. The timing of the recommendations may correspond with the timing of sensor data indicating that a particular user is currently riding a bicycle; [0073] The method may, at 606, also go beyond product recommendations to provide actual sensor data indicating customer satisfaction. For example, if an insomniac follows a recommendation for a better pillow by buying one, the subsequently collected and perhaps ongoing sensor data that indicates the customer is indeed sleeping better may serve as a strong but silent endorsement of the product. Data indicating actual product or service effectiveness may therefore be used to further refine future recommendations. Similarly, the purchaser of recommended new shoes may increase the amount of running performed each week, which may lead to increased weight loss or other positive effects that may prove influential for a seller.)
detect a change in a value of a predetermined data item in the physical data; ([0073] The method may, at 606, also go beyond product recommendations to provide actual sensor data indicating customer satisfaction. For example, if an insomniac follows a recommendation for a better pillow by buying one, the subsequently collected and perhaps ongoing sensor data that indicates the customer is indeed sleeping better may serve as a strong but silent endorsement of the product. Data indicating actual product or service effectiveness may therefore be used to further refine future recommendations. Similarly, the purchaser of recommended new shoes may increase the amount of running performed each week, which may lead to increased weight loss or other positive effects that may prove influential for a seller.)
extract purchase history of a product or a service ([0059] For example, suppose sensor data for particular user indicates this user does not sleep well, but instead does an unusual amount of tossing and turning, compared for example to the user's own history or to the histories of other users. This user may be associated with a group of other users who also share this physical activity pattern. The server 412 may then determine that products and services that were previously purchased by the group of other users might be of interest to the particular user. The relevant products or services may be related to sleeping, and may include, for example, pillows, sheets, sleeping pills, or late-night entertainment content. Thus, the combination of user physical activity patterns and previous purchases of the user and/or other users sharing similar physical activity patterns may define a market segment and corresponding products or services relevant to that market segment.) that changes the value of the predetermined data item from the payment data of the user regarding whom the change in the value of the predetermined data item has been detected; ([0073] The method may, at 606, also go beyond product recommendations to provide actual sensor data indicating customer satisfaction. For example, if an insomniac follows a recommendation for a better pillow by buying one, the subsequently collected and perhaps ongoing sensor data that indicates the customer is indeed sleeping better may serve as a strong but silent endorsement of the product. Data indicating actual product or service effectiveness may therefore be used to further refine future recommendations. Similarly, the purchaser of recommended new shoes may increase the amount of running performed each week, which may lead to increased weight loss or other positive effects that may prove influential for a seller.)
generate recommendation information for recommending […] product or service; ([0061] The server 412 may send the recommendations for relevant products or services to an application 414.)
transmit a signal to user terminal device of other members of the group to notify the other members of the group of the generated recommendation information. ([0062] Application 414 may display a recommendations window 416, for example. Application 414 may also provide an icon or link to enable a user to readily take an action in response to the recommendation(s). For example, in icon 418, the user may be able to directly buy a suggested item from a list of suggested items. Application 414 may then transmit this user choice to a networked publication system or ecommerce system via instruction 420. and see [0064- notify other users in the group of users)
While Harve discloses determining a market segment of similar users based on their purchase history and then providing recommendations based on the determination of an improvement for one of the users who has used the product, in order to make the recommendation, the reference does not explicitly disclose:
specify, from the extracted purchase history up to a time traced back by a predetermined period from a time when the value of the predetermined data item was changed a product or a service that changes the value of the predetermined data item, by referring to a list in which products or services for changing values of predetermined data items are predefined for each of the predetermined data items;
generate recommendation information for recommending the specified product or service;
However Kartoun teaches:
specify, from the extracted purchase history up to a time traced back by a predetermined period from a time when the value of the predetermined data item was changed ([Col. 23 lines 8-23] In particular, the cognitive system may comprise a commercial transaction analysis system 100 and a health services computing system 130 which operate in conjunction to obtain and analyze commercial transaction data from commercial transaction data sources and provide cognitive evaluations of the commercial transaction data gathered from such commercial transaction data sources, secondary lifestyle information from secondary sources, and patient EMR data from clinical data sources, to evaluate a patient's medical condition, lifestyle behavior (e.g., habits), and adherence to treatments, which may include evaluations with regard to changes in such since a last visit or encounter between the physician and the patient. This functionality may be performed periodically, according to a predetermined schedule, or in response to the detection of particular events.) a product or a service that changes the value of the predetermined data item, by referring to a list in which products or services for changing values of predetermined data items are predefined for each of the predetermined data items; ([Col. 16 lines 1-25] It should be appreciated that, in some illustrative embodiments, the commercial transaction analysis system 100 may maintain patient profile data structures (not shown) for each of the patients that have registered for utilization of the commercial transaction analysis system 100. These patient profile data structures may store historical data regarding the results of the analyzer modules 112-119 generated for the patient over a predetermined period of time. This historical data may be evaluated by the analyzer modules 112-119 in combination with new commercial transaction data and secondary source lifestyle information obtained for a current time period so as to be able to perform such behavior, habit, or trend identifications for the patient. Thus, for example, in a previous month, the patient's nutritional behavior was to eat at fast food restaurants for every other meal, and in the present month, the patient has cut that back to only twice a week which indicates a trend or behavior heading in the direction of improved healthy eating habits even though they may not still be considered “healthy” eating habits. This information may be relayed to the physician via the physician interface engine 132 and/or may be the basis for performing other cognitive operations via the cognitive analysis pipelines 136.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the recommendation process of Harve to include specify, from the extracted purchase history up to a time traced back by a predetermined period from a time when the value of the predetermined data item was changed a product or a service that changes the value of the predetermined data item, by referring to a list in which products or services for changing values of predetermined data items are predefined for each of the predetermined data items; generate recommendation information for recommending the specified product or service; , as taught in Kartoun, in order to allow the patient's commercial transactions may be utilized as a way to discern lifestyle behavior patterns of the patient automatically which may then be used to interact with the patient regarding the impact of their lifestyle behavior patterns on their health, specific medical conditions, and/or treatments. (Col. 2 lines 28-35).
Regarding claim 2, Harve in view of Kartoun teaches the limitations set forth above and Harve further discloses:
Wherein the computer is further configured to generate the recommendation information including an amount of change in the value of the predetermined data item. ([0073] The method may, at 606, also go beyond product recommendations to provide actual sensor data indicating customer satisfaction. For example, if an insomniac follows a recommendation for a better pillow by buying one, the subsequently collected and perhaps ongoing sensor data that indicates the customer is indeed sleeping better may serve as a strong but silent endorsement of the product. Data indicating actual product or service effectiveness may therefore be used to further refine future recommendations. Similarly, the purchaser of recommended new shoes may increase the amount of running performed each week, which may lead to increased weight loss or other positive effects that may prove influential for a seller.)
Regarding claim 5, Harve in view of Kartoun teaches the limitations set forth above and Harve further discloses:
Wherein the computer is further configured to generate the recommendation information further including information for recommending products or services related to […]. ([0059] For example, suppose sensor data for particular user indicates this user does not sleep well, but instead does an unusual amount of tossing and turning, compared for example to the user's own history or to the histories of other users. This user may be associated with a group of other users who also share this physical activity pattern. The server 412 may then determine that products and services that were previously purchased by the group of other users might be of interest to the particular user. The relevant products or services may be related to sleeping, and may include, for example, pillows, sheets, sleeping pills, or late-night entertainment content. Thus, the combination of user physical activity patterns and previous purchases of the user and/or other users sharing similar physical activity patterns may define a market segment and corresponding products or services relevant to that market segment.)
While Harve discloses determining a market segment of similar users based on their purchase history and then providing recommendations based on the determination of an improvement for one of the users who has used the product, in order to make the recommendation, the reference does not explicitly disclose:
Wherein the computer is further configured to generate the recommendation information further including information for recommending products or services related to the specified product or service.
However Kartoun teaches:
Wherein the computer is further configured to generate the recommendation information further including information for recommending products or services related to the specified product or service. [Col. 18 lines 65-67] Moreover, in some illustrative embodiments, this change information may be used to drive further cognitive operations such that the cognitive analysis pipeline(s) 136 may generate treatment recommendations for the patient, recommended modifications to existing treatments, or even specific targeted warning messages, notifications, or the like, to be transmitted to the patient's client device or physician's client device 135. [Col. 21 lines 45-55] Alternatively, the cognitive analysis pipelines 136 may evaluate previously prescribed treatments and determine the areas of lifestyle behavior information where the patient is deviating from the previously prescribed treatment and determine an adjustment to bring the patient back into conformance with the prescribed treatment, e.g., increase activity by getting a gym membership and using it
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the recommendation process of Harve to include wherein the computer is further configured to generate the recommendation information further including information for recommending products or services related to the specified product or service , as taught in Kartoun, in order to allow the patient's commercial transactions may be utilized as a way to discern lifestyle behavior patterns of the patient automatically which may then be used to interact with the patient regarding the impact of their lifestyle behavior patterns on their health, specific medical conditions, and/or treatments. (Col. 2 lines 28-35).
Regarding claim 6, Harve discloses the limitations set forth above and further discloses:
acquires data of an exercise history of each of the plurality of members, generate the recommendation information for recommending, […] the product or the service selected based on the exercise history of the user regarding whom the change in the value of the predetermined data item has been detected. ([0060] Similarly, a market segment of users may include bicyclists, and server 412 may provide them with bicycling related recommendations. The timing of the recommendations may correspond with the timing of sensor data indicating that a particular user is currently riding a bicycle. In another example, the timing of the recommendations may correspond with the timing of sensor data indicating that a particular user has just finished riding a bicycle. In either case, the recommendations may be for bicycling related goods and services, such as sports drinks, vitamins and other supplements, exercise equipment, nutrition-based diet suggestions, information regarding competitions, apparel, sports event tickets, gymnasium memberships, shoes, subscriptions to health newsletters or magazines, and so forth. Also see server 412 in Figure 4 and [0059])
While Harve discloses determining a market segment of similar users based on their purchase history and then providing recommendations based on the determination of an improvement for one of the users who has used the product, in order to make the recommendation, the reference does not explicitly disclose:
the recommendation information for recommending, among the specified products or the services,
However Kartoun teaches:
the recommendation information for recommending, among the specified products or the services, [Col. 18 lines 65-67] Moreover, in some illustrative embodiments, this change information may be used to drive further cognitive operations such that the cognitive analysis pipeline(s) 136 may generate treatment recommendations for the patient, recommended modifications to existing treatments, or even specific targeted warning messages, notifications, or the like, to be transmitted to the patient's client device or physician's client device 135. [Col. 21 lines 45-55] Alternatively, the cognitive analysis pipelines 136 may evaluate previously prescribed treatments and determine the areas of lifestyle behavior information where the patient is deviating from the previously prescribed treatment and determine an adjustment to bring the patient back into conformance with the prescribed treatment, e.g., increase activity by getting a gym membership and using it
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the recommendation process of Harve to include the recommendation information for recommending, among the specified products or the services, as taught in Kartoun, in order to allow the patient's commercial transactions may be utilized as a way to discern lifestyle behavior patterns of the patient automatically which may then be used to interact with the patient regarding the impact of their lifestyle behavior patterns on their health, specific medical conditions, and/or treatments. (Col. 2 lines 28-35).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Harve in view of Kartoun in further view of Kim (US 20100088151).
Regarding claim 3, Harve in view of Kartoun teaches the limitations set forth above. While Harve in view of Kartoun teaches the recommendation of products to a community of members based on the improvement to a health component of a user who purchased and used the product resulting in the improved health change/benefit (one example shown in [0073]), and the specified product or service from the transaction information [Col. 16 lines 1-25], the combination does not explicitly disclose:
Wherein the computer is further configured to generate the recommendation information including a purchase frequency or a purchase volume by the user regarding whom the change in the value of the predetermined data item has been detected regarding products specified by the extracted purchase history.
However Kim teaches:
Wherein the computer is further configured to generate the recommendation information including a purchase frequency or a purchase volume by the user regarding whom the change in the value of the predetermined data item has been detected regarding products specified by the extracted purchase history. ([0016] The generating of the image recommendation list may include extracting the specific number of upper multimedia image contents, in which a frequency of purchase is high, from the set neighborhood to generate the image recommendation list.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data utilized in the recommendation system of Harve in view of Kartoun to include Wherein the computer is further configured to generate the recommendation information including a purchase frequency or a purchase volume by the user regarding whom the change in the value of the predetermined data item has been detected regarding products specified by the extracted purchase history, as taught in Kim, in order to provide good quality recommendations (paragraph 0067).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Harve in view of Kartoun in further view Bender (US 20190385199).
Regarding claim 4, Harve in view of Kartoun teaches the limitations set forth above. While Harve in view of Kartoun teaches the recommendation of products to a community of members based on the improvement to a health component of a user who purchased and used the product resulting in the improved health change/benefit (one example shown in [0073]), and the specified product or service from the transaction information [Col. 16 lines 1-25], the combination does not explicitly disclose:
wherein the computer is further configured to generate the recommendation information including a usage frequency by or a usage period of the user regarding whom the change in the value of the predetermined data item has been detected regarding services specified by the extracted purchase history.
However Bender teaches:
wherein the computer is further configured to generate the recommendation information including a usage frequency by or a usage period of the user regarding whom the change in the value of the predetermined data item has been detected regarding services specified by the extracted purchase history.([0032] Moreover, embodiments of the receiving module 131 may receive, obtain, or otherwise collect a machine usage data from a plurality of internet-connected exercise machines 111 associated with the plurality of registered users. For instance, one or more exercise machine 111 belonging to the registered user may transmit machine usage data and machine identifying information to the computing system 120 to be used to calculate a registered user fitness metric. Embodiments of the machine usage data may be an average exercise time per session, a number of exercise sessions per day, per week, per months, etc., an intensity of each exercise session of the exercise machine 111 by the registered user, a type of exercise program used by the registered user when exercising, a period of inactivity between workouts, and the like. The machine usage data received by the receiving module 131 may be any data related to the operation of the exercise machine 111 by the registered user. And see [0040, 41]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data utilized in the recommendation system of Harve in view of Kartoun to include wherein the computer is further configured to generate the recommendation information including a usage frequency by or a usage period of the user regarding whom the change in the value of the predetermined data item has been detected regarding services specified by the extracted purchase history, as taught in Bender, in order to provide recommendations in the area of fitness that are related to other users of similar fitness levels (paragraph 0016).
Claim 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Harve in view of Kartoun in further view Steves (US 20150294327).
Regarding claim 9, Harve in view of Kartoun teaches the limitations set forth above. While Harve in view of Kartoun teaches the recommendation of products to a community of members based on the improvement to a health component of a user who purchased and used the product resulting in the improved health change/benefit (one example shown in [0073]), and the specified product or service from the transaction information [Col. 16 lines 1-25], the combination does not explicitly disclose:
wherein the computer is configured to generate, using the payment data, the group of the plurality of members whose rates of purchase amounts or rates of purchase volumes for each category of products or services are similar to one another
However Steves teaches:
wherein the computer is configured to generate, using the payment data, the group of the plurality of members whose rates of purchase amounts or rates of purchase volumes for each category of products or services are similar to one another [0038] The cluster detection application 125 can analyze purchase histories, purchasing trends, browsing history and other data to identify clusters of users associated with certain products or categories of products. In other words, the cluster detection application 125 can group customers according to products they are buying or in which they have an interest. For one or more items available via the electronic commerce application 119, the cluster detection application 125 can identify a subset of users that purchase or demonstrate an interest in the item to a greater degree than other users. As one example, the cluster detection application 125 can identify customers where a certain percentage of their purchases (e.g., by price, volume, etc.) exceed a threshold. As another example, the cluster detection application 125 can identify customers that exhibit a higher percentage of their purchases relative to other users. In one example, the cluster detection application 125 can identify a cluster for a group of customers who make ten percent or more of their purchases in a “gluten-free” product category, whereas only three percent of other users act similarly. Additionally, the cluster detection application 125 can also be configured to identify such a cluster if these do not purchase products that are not considered “gluten-free.” In other words, the cluster detection application 125 designates customers who are a part of the “gluten-free” cluster if they purchase “gluten-free” products while purchasing less than a configurable threshold of products that are not considered “gluten-free.” [0039] As another example, the cluster detection application 125 can identify an “organic” cluster for a group of customers who make fifty percent or more of their food purchases by buying products that are designated as “organic,” whereas other customers exhibit a purchase history for food products that shows less than fifty percent of “organic” products.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data utilized in the recommendation system of Harve in view of Kartoun to include wherein the computer is configured to generate, using the payment data, the group of the plurality of members whose rates of purchase amounts or rates of purchase volumes for each category of products or services are similar to one another, as taught in Steves, in order to improve subsequent recommendations and targeting campaigns (paragraph 002).
Regarding claim 10, Harve in view of Kartoun teaches the limitations set forth above. While Harve in view of Kartoun teaches the recommendation of products to a community of members based on the improvement to a health component of a user who purchased and used the product resulting in the improved health change/benefit (one example shown in [0073]), and the specified product or service from the transaction information [Col. 16 lines 1-25], the combination does not explicitly disclose:
wherein the computer is configured to generate, using the payment data, the group of the plurality of members whose purchase amounts or purchase volumes of specific products or services exceed a threshold.
However Steves teaches:
wherein the computer is configured to generate, using the payment data, the group of the plurality of members whose purchase amounts or purchase volumes of specific products or services exceed a threshold. [0040] The cluster detection application 125 can also identify clusters on a product-by-product basis rather than on a category-by-category basis. In this way, the cluster detection application 125 can identify a cluster without detecting a product attribute or assignment with which the product is associated. Therefore, a cluster can be identified, which can inform a decision regarding an assignment 171 to which the product can be associated. The cluster detection application 125 can also identify brand preferences as well as cluster users based upon a brand preference. In other words, the cluster detection application 125 can determine whether one or more users exhibits a preference towards a particular brand of products to the exclusion of other brands in the same or similar product category. For example, if a subset of users purchases one particular brand of digital camera as well as digital camera accessories to the exclusion of another brand of camera, then the cluster detection application 125 can create a cluster of users based upon camera brand preference. And see [0041]
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data utilized in the recommendation system of Harve in view of Kartoun to include wherein the computer is configured to generate, using the payment data, the group of the plurality of members whose purchase amounts or purchase volumes of specific products or services exceed a threshold, as taught in Steves, in order to improve subsequent recommendations and targeting campaigns (paragraph 002).
Response to Arguments
Applicant’s arguments, filed 8/25/2025, with respect to the 112(f) claim interpretation have been fully considered and are persuasive. The112(f) claim interpretation has been withdrawn.
Applicant's arguments filed 8/25/2025 have been fully considered but they are not persuasive for the reasons set forth below.
With respect to the remarks directed to 35 USC 101, Step 2A prong 1, the examiner first asserts that the rejection has not characterized the claim limitations as being able to be performed by the human mind, but rather the analysis of transaction data for making the determination of recommendations. This is a fundamental economic practice falling into the enumerated group of sales and marketing activities; “The courts have used the phrases "fundamental economic practices" or "fundamental economic principles" to describe concepts relating to the economy and commerce.” (MPEP 2106.04(a)(II)(A).
With respect to the remarks directed to 35 USC 101, Step 2A prong 2, the computer provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The examiner maintains that the computer recited in the claims is merely a generic computing element that is recited at a high level of generality for carrying out the abstract idea. The improvement does not lie in the technology itself. At most the improvement (problem to be solved by the claimed invention) lies in the business process.
With respect to the remarks directed to 35 USC 101, Step 2B, the examiner asserts that processing data of users with similar purchase behavior, recommendation information for recommending products or services likely to be purchase by the member in the group that can improve the value of the data item is not a technical solution, but again, at most an improvement (problem to be solved by the claimed invention) in the business process. The rejection did not characterize any of the limitations as well-understood routine and conventional and therefore the remarks directed to this characterization are moot.
For at least these reasons, the claims remain rejected under 35 USC 101.
With respect to the remarks directed to the previous rejection under 35 USC 102 citing Harve, the examiner has updated the rejection above in light of the amendment. The claims are now rejected under 35 USC 103 citing Harve in view of Kartoun. While the examiner maintains the previously presented limitation were disclosed in Harve, the examiner is persuaded Harve alone does not teach the claims as amended and now relies also on the teachings of Kartoun for at least the newly added limitation directed to “specifying a product of service that changes a value of data item”. The dependent claims and newly added claims 9-10 are also addressed with respect to the newly added prior art in light of the claim amendment. The claims remain rejected under 35 USC 103.
Relevant Art Not Cited
Chakraborty (US 2021003569) discloses generating a user health rating based on analyzing purchase trend data.
Stein US20140280001 discloses using personal profiles to return items of interest to a user.
Melcher US 20150278912 discloses receiving a plurality of attributes from multiple sources to match users to items based on meshing the data from the sources.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 9/25/2025