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 .
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 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.
Status of Claims
Claims 1-20 remain pending, and are rejected.
Response to Arguments
Applicant’s arguments filed on 3/18/2026 with respect to the rejection under double patenting have been fully considered, and are persuasive.
Applicant’s arguments filed on 3/18/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 3/18/2026 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 15-16 of the Applicant’s Remarks, arguments are made that the claims are not directed to a judicial exception because the claims as a whole integrate the exception into a practical application by improving the functioning of the computer system by increasing available memory storage capacity by removing unnecessary information from information stored in a memory storage system and increasing available network bandwidth by reducing a number of database requests. Applicant cites specification paragraph [0087] disclosing that the invention is rooted in computer technology to overcome existing problems in database systems to increase available bandwidth, reduce network traffic, and efficiently manage databases.
Examiner respectfully disagrees. The Examiner did not indicate that the claims would overcome the rejections in the interview conducted on 3/3/2026 (see the interview summary mailed on 3/5/2026), and had indicated that the proposed amendments would not overcome the rejections. The claims and specification do not disclose any actual changes to any computing functionality. The purported improvements to computer systems of increasing available memory storage capacity by removing unnecessary information from information stored in a memory storage system and increasing available network bandwidth by reducing a number of database requests are not from any technical changes or improvements, but merely from a more efficient abstract idea. The alleged improvements would not be present in any other application. The increased memory storage capacity is not from any improvements to how a computer stores and retrieves data, but merely from less information of the abstract idea that is being stored on a generic storage device. The increased available network bandwidth is not from any improvement to network technology, but only from not transmitting some information. The abstract idea itself merely requires a lesser amount of information. The computer operates in a generic manner, and merely has less information it needs store and transmit. The specification does not provide any more support for any technical improvements, merely stating the alleged improvements without any further detail, except to the abstract idea utilizing less information as described above.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
Applicant’s arguments filed on 3/18/2026 with respect to the rejection under 35 U.S.C. 102 and 103 have been fully considered, but are moot in light of new grounds of rejection. Applicant’s amendments necessitated new grounds of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-10 are directed to a system, which is an apparatus. Claims 11-20 are directed to a method, which is an article of manufacture. Therefore, claims 1-20 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 1 as representative, claim 1 sets forth the following limitations reciting the abstract idea of removing items that the user no longer purchases at regular intervals:
determining a personal replenishment cycle for an item of a set of items based on an estimated time period of how often communicate the user select the item via a cart;
removing, from information regarding the set of items, information regarding a first item based on the stopping to communicate to select the first item via the cart;
reducing requests for the set of items based on executing rules on the personal replenishment cycle for the item by identifying an elapsed time since select the item via the cart.
The recited limitations above set forth the process for removing items that the user no longer purchases at regular intervals. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to determining item replenishment cycles for a user and identifying items the user has stopped purchasing to remove (see specification [0030] disclosing the problem of the difficulty of estimating the user’s personal replenishment cycle), which is a sales and marketing endeavor.
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Returning to representative claim 1, Examiner acknowledges that claim 1 recites additional elements, such as:
one or more processors;
one or more non-transitory computer-readable media storing computing instructions;
reducing a number of database requests;
generate instructions for providing a graphical user interface on a display of a computer of one or more computers;
one or more computers are used to communicate via a network;
information stored in a memory storage system;
a number of database requests;
Taken individually and as a whole, claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than apply the judicial exception on a general purpose computer.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite one or more processors and one or more non-transitory computer-readable media storing computing instructions, these elements are recited with a very high level of generality, and recited in passing as a preamble to the abstract idea of the claims. Specification paragraph [0022] defines the processor as any type of computational circuit, such as a microprocessor, a microcontroller, a graphics processor, or any other type of processor or processing circuit. Specification paragraph [0020] discloses the memory as being any of ROM, RAM, EEPROM, etc. The specification shows that these additional elements may be any generic component, and are used merely to implement the abstract idea on a computing device, and provide a general link to a computing environment. The databases are also any generic database, as evidenced in specification paragraph [0043], which discloses the database can comprise a structured collection of data and can be managed by any suitable database management system, such as MySQL, PostgreSQL database, Microsoft SQL server, etc. It is evident that the databases function as they generically do, and any reduction in database requests does not reflect an improvement in any computer technology, but merely represents less needs for requesting for information for the abstract idea. The database requests also do not present any more than a request for information. The database requests do not change any technology of how a computer stores and retrieves data in memory or how it transmits and receives data.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106.04(d)).
Step 2B:
Returning to claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 11 (method): Claim 11 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 11 is rejected under at least similar rationale as provided above regarding claim 1.
Dependent claims 2-10 and 12-20 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm of removing items that the user no longer purchases at regular intervals. Thus, each of claims 2-10 and 12-20 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-10 and 12-20 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-10 and 12-20 rely on at least similar elements as recited in claim 1. Further additional elements (e.g., a user database system (claim 4)) are also acknowledged; however, the additional elements of claims 2-10 and 12-20 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-10 and 12-20 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-10 and 12-20 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 2-10 and 12-20 do not add “significantly more” to the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable by Carr (US 20160125506 A1) in view of Chen (US 20160125506 A1).
Regarding Claim 1: Carr discloses a system comprising:
one or more processors; (Carr: [0021] – “Order management system 114 includes a communication module 202, a processor”).
one or more non-transitory computer-readable media storing computing instructions; (Carr: [0016] – “These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner”).
determining a personal replenishment cycle for an item of a set of items used to generate instructions for providing a graphical user interface on a display of a computer of one or more computers, based on an estimated time period of how often the one or more computers are used to communicate, via a network, with a web server to select the item via an electronic cart; (Carr: [0023] – “user profile manager 214 may store information regarding user names, user accounts, user buying habits, user delivery address, user payment methods, and the like. An order history manager 216 identifies items purchased on a regular basis by particular customers, and frequencies of those purchases. Order history manager 216 may also track other buying habits, item preferences, brand preferences, etc. associated with any number of customers”; Carr: [0024] – “Communication module 302 allows order processing system 122 to communicate with other systems, such as communication networks, other servers, order management system 114, and the like”; Carr: [0022] – “order creation module 210 manages online shopping carts, order checkout processes, shipping policies, delivery policies, and the like”). In summary, the system determines how often an item is purchased, and the purchases are tracked and stored based on the communications between the system and other systems, including the user device (see also: Carr: Fig. 1, #104,108,102,118,120 displaying a user device communicating with the online marketplace through communications networks).
removing, from information stored in a memory storage system regarding the set of items, information regarding a first item based on the one or more computers stopping to communicate, via the network, with the web server to select the first item via the electronic cart; (Carr: [0032] – “The number of times a customer has declined to add an item to their order may be an indication that the customer no longer wants to purchase that item. For example, the customer may no longer like the item, may have selected a different type of item, may have selected a different brand of the item, may have stopped using the item, or may be purchasing the item from a different merchant. In some embodiments, method 500 may limit the number of times an item is suggested to a particular customer. For example, if a customer declines a suggestion to add toothpaste to their order four consecutive times, the system may no longer suggest toothpaste (e.g., that particular brand of toothpaste) to the customer”; Carr: claim 6 - “removing the particular item from the purchase option presented to the customer responsive to determining that the number of declined suggestions for the particular item exceeds the threshold value”).
reducing a number of database requests for the set of items based on executing a set of rules on the personal replenishment cycle for the item. (Carr: [0032] – “the customer may no longer like the item, may have selected a different type of item, may have selected a different brand of the item, may have stopped using the item, or may be purchasing the item from a different merchant. In some embodiments, method 500 may limit the number of times an item is suggested to a particular customer. For example, if a customer declines a suggestion to add toothpaste to their order four consecutive times, the system may no longer suggest toothpaste”). Carr discloses removing the item from the purchase options and no longer suggesting the items, which would result in less requests to the database for information of that item.
Carr does not explicitly teach by identifying an elapsed time since the one or more computers were used to select the item via the electronic cart. Notably, however, Carr does disclose determining the last date the customer purchased an item (Carr: [0031]). Carr merely does not disclose where the elapsed time since the last purchase is the factor of determining whether a customer no longer wants the item.
To that accord, Chen does teach by identifying an elapsed time since the one or more computers were used to select the item via the electronic cart. (Chen: [0075] – “Retailers may then recognize when a consumer has stopped purchasing a particular item, or when greater lapses of time occur between purchases of a particular item”).
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 invention of Carr disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the elapsed time being used in the determination as taught by Chen. One of ordinary skill in the art would have been motivated to do so in order to make future offers to customers as necessary (Chen: [0075]).
Regarding Claim 2: Carr in view of Chen discloses the limitations of claim 1 above.
Carr further discloses the estimated time period is based on historical sales data; (Carr: [0023] – “An order history manager 216 identifies items purchased on a regular basis by particular customers, and frequencies of those purchases. Order history manager 216 may also track other buying habits, item preferences, brand preferences, etc. associated with any number of customers”).
Regarding Claim 3: Carr in view of Chen discloses the limitations of claim 1 above.
Carr further discloses wherein:
reducing the number of database requests increases a network bandwidth of the system; (Carr: [0032] – “the customer may no longer like the item, may have selected a different type of item, may have selected a different brand of the item, may have stopped using the item, or may be purchasing the item from a different merchant. In some embodiments, method 500 may limit the number of times an item is suggested to a particular customer. For example, if a customer declines a suggestion to add toothpaste to their order four consecutive times, the system may no longer suggest toothpaste”). Removing the item from the purchase options and no longer suggesting the items would result in less requests to the database for information of that item and data being transmitted through the network.
identifying an elapsed time since the user bought the item from a retailer; (Carr: [0031] – “Method 500 continues by determining the last date the customer purchased each of the identified items”).
estimating a number of times the user has replenished (i) the item or (ii) remaining items of the set of items; (Carr: [0031] – “Method 500 also identifies at 508 a number of times the item has been suggested to the customer, but the customer declined to add the item to their order. In some embodiments, method 500 monitors the number of times a customer has declined to add an item to their order since the last time the customer purchased that item. If the number of times a customer has declined to add an item exceeds a threshold value, that item may not be suggested to the customer”). In summary, the purchases of the items are tracked in the order data, and the number of times the item has been replenished is merely the difference of the total number of times the item was suggested and the number of times it was declined.
Regarding Claim 13: Claim 13 recites substantially similar limitations as claim 3. Therefore, claim 13 is rejected under the same rationale as claim 3 above.
Regarding Claim 11: Claim 11 recites substantially similar limitations as claim 1. Therefore, claim 11 is rejected under the same rationale as claim 1 above.
Regarding Claim 12: Claim 12 recites substantially similar limitations as claim 2. Therefore, claim 12 is rejected under the same rationale as claim 2 above.
Regarding Claim 13: Claim 13 recites substantially similar limitations as claim 3. Therefore, claim 13 is rejected under the same rationale as claim 3 above.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Carr (US 20160125506 A1) and Chen (US 20160125506 A1), in view of Rangan (US 20170345079 A1).
Regarding Claim 4: The combination of Carr and Chen discloses the limitations of claim 1 above.
Carr does not explicitly teach transferring an identification of the item and an estimated next purchase date to a user database system.
Notably, however, Carr does disclose tracking how often the use purchases and item and how long it’s been since their last purchase of an item (Carr: [0030-0031]).
To that accord, Rangan does teach a system comprising:
transferring an identification of the item and an estimated next purchase date to a user database system. (Rangan: [0079] – “The user profile 160 includes the shopping list 170 that is dynamically put together based on the scan data received from the user, push notifications received from the smart appliances 142, 144 and projected dates for replenishment of staple products routinely purchased by the user as determined by the product monitor 128. The shopping list 170 not only displays the days until the supplies last, but provides the user an opportunity to determine if a product should be marked for purchase”).
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 invention of the combination of Carr and Chen disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the analyzing the items at a point of sale to estimate a time period of repurchase and transfer an identification of the item to a user database as taught by Rangan. One of ordinary skill in the art would have been motivated to do so in order to allow the user to determine of a product should be marked for purchase and move the products up on the shopping list (Rangan: [0079]).
Regarding Claim 14: Claim 14 recites substantially similar limitations as claim 4. Therefore, claim 14 is rejected under the same rationale as claim 4 above.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Carr (US 20160125506 A1), Chen (US 20160125506 A1), and Rangan (US 20170345079 A1), in view of White (US 20130282626 A1).
Regarding Claim 5: The combination of Carr, Chen, and Rangan discloses the limitations of claim 4 above.
identifying a number of replenishments for the item that a, of the computer, has made since the user last bought the item from a retailer; (Carr: [0031] – “Method 500 continues by determining the last date the customer purchased each of the identified items at 506. For example, the system may determine that the customer last purchased toothpaste six weeks ago and last purchased bananas 10 days ago. Method 500 also identifies at 508 a number of times the item has been suggested to the customer, but the customer declined to add the item to their order. In some embodiments, method 500 monitors the number of times a customer has declined to add an item to their order since the last time the customer purchased that item”). In summary, the purchases of the items are tracked in the order data, and the number of times the item has been replenished is merely the difference of the total number of times the item was suggested and the number of times it was declined.
identifying a mean replenishment cycle for the user and the item; (Carr: [0030] – “Method 500 identifies items regularly purchased by the customer (but not in the current order) and the purchase frequency of those items”). The purchase frequency represents a mean replenishment cycle.
assuming a number of inter-replenishment times for the (user, item) pair from historical sales data of the retailer; (Carr: [0031] – “Method 500 also identifies at 508 a number of times the item has been suggested to the customer, but the customer declined to add the item to their order. In some embodiments, method 500 monitors the number of times a customer has declined to add an item to their order since the last time the customer purchased that item”). In summary, the purchases of the items are tracked in the order data, and the number of times the item has been replenished is merely the difference of the total number of times the item was suggested and the number of times it was declined.
The combination does not explicitly teach a system comprising:
assuming a relationship of a model based on using independent random variables that are (i) mutually independent of one another and (ii) identically distributed random variables for a (user, item) pair with an expectation and a variance;
estimating parameters of the model.
Notably, however, Carr does disclose identifying item and customer purchasing patterns from historical data (Carr: [0023]).
To that accord, White does teach a system comprising:
assuming a relationship of a model based on using independent random variables that are (i) mutually independent of one another and (ii) identically distributed random variables for a (user, item) pair with an expectation and a variance; (White: [0122] – “An experimental design is a systematic plan for executing a controlled experiment. The experimental design is a subset of the entire attribute space with usually far fewer unique treatments than the complete attribute space. The creation of a design is intimately related to the attribute space under examination and the model form that is hypothesised to explain the phenomena under examination (through estimation of model parameters). Typically the attribute space is obtained from the problem definition (eg signature), but for a given attribute space there may be various appropriate experimental designs and models which may be used to obtain estimates of the model parameters (eg coefficients .beta.). For a given design, different models vary in how they perform estimation of model parameters and their variance, and thus different models have different strengths and weaknesses”). In summary, White teaches a model for user behavior using mutually independent and identically distributed variables, and variance of parameters.
estimating parameters of the model. (White: [0122] – “The creation of a design is intimately related to the attribute space under examination and the model form that is hypothesised to explain the phenomena under examination (through estimation of model parameters). Typically the attribute space is obtained from the problem definition (eg signature), but for a given attribute space there may be various appropriate experimental designs and models which may be used to obtain estimates of the model parameters”).
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 invention of the combination of Carr, Chen, and Rangan disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the model based on independent random variables that are mutually independent and identically distributed, and estimating parameters as taught by White. One of ordinary skill in the art would have been motivated to do so in order to accurately predict human behavior (White: [0008]).
Regarding Claim 15: Claim 15 recites substantially similar limitations as claim 5. Therefore, claim 15 is rejected under the same rationale as claim 5 above.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Carr (US 20160125506 A1), Chen (US 20160125506 A1), Rangan (US 20170345079 A1), and White (US 20130282626 A1), in view of Kazerouni (US 20180129760 A1).
Regarding Claim 6: The combination of Carr, Rangan, and White discloses the limitations of claim 5 above.
The combination does not explicitly teach the system comprising:
solving an optimization problem for the (user, item) pair, to obtain a set of estimates for the (user, item) pair, based on:
an estimate of a mean of the set of estimates;
an estimate of a standard deviation of the personal replenishment cycle for the user and the item.
Notably, however, Carr does disclose identifying item and customer purchasing patterns from historical data (Carr: [0023]), and White discloses a model for user behavior, estimating parameters, such as variance (White: [0122]).
To that accord, Kazerouni does teach the system comprising:
solving an optimization problem for the (user, item) pair, to obtain a set of estimates for the (user, item) pair, based on: (Kazerouni: [0071] – “As previously described, fixed-horizon hypothesis testing techniques employ a stopping time (i.e., horizon), which is determined based on the Minimum Detectable Error (MDE, defined as [00001].Math.mA-mB.Math.mA), and the test continues until that horizon is reached, i.e., a specified number of samples. It is only at that time where conclusions about the null hypothesis can be made, and no decision can be made before that, even if the data strongly suggests rejection/acceptance of the null hypothesis. However, the MDE is not known a priori (i.e., in a way based on theoretical deduction rather than empirical observation) and hence the horizon is generally set significantly higher than its optimal value to guarantee the desired error bounds in real world scenarios. Thus, the calculated horizon might have a large value (i.e., number of samples) which causes the test to continue until that value is reached”).
an estimate of a mean of the set of estimates; (Kazerouni: [0082] – “A means estimation module 602 is then implemented to estimate a mean value 604 for each option of the plurality of options described in the testing data 314. In one example, this is performed using an empirical technique that is based on a central limit theorem (CLT). In probability theory, a central limit theorem specifies that when independent random variables are added, a sum of these variables tends toward a normal distribution (e.g., a bell curve) even if the original variables themselves are not normally distributed”).
an estimate of a standard deviation of the personal replenishment cycle for the user and the item. (Kazerouni: [0079] – “the model generation module 306 generates an ensemble model (e.g., mixture model) as formed as a mixture of a plurality of sub-models based on distributions of the effect of the user interactions described by historical data 302 (block 402)… and the variance of the outcomes for option “A” is: v.sub.A=(1−p.sub.A)(e.sup.σ.sup.A.sup.2−1)e.sup.2μ.sup.A.sup.+σ.sup.A.sup.2. Similarly, the data observed for option “B” may be modeled as the same mixture model with parameters (p.sub.B,μ.sub.B,σ.sub.B.sup.2)”). The variance is merely the square of standard deviation, so estimating the variance is also an estimation of the standard deviation.
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 invention of the combination of Carr, Chen, Rangan, and White disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the optimizing the problem based on estimating a mean and standard deviation as taught by Kazerouni. One of ordinary skill in the art would have been motivated to do so in order to guarantee the desired error bounds in real world scenarios (Kazerouni: [0071]).
Regarding Claim 16: Claim 16 recites substantially similar limitations as claim 6. Therefore, claim 16 is rejected under the same rationale as claim 6 above.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable by Carr (US 20160125506 A1) and Chen (US 20160125506 A1), in view of Wang (US 10,671,679 B2), and in further of Mukherjee (US 20160055501 A1).
Regarding Claim 7: The combination of Carr and Chen discloses the limitations of claim 1 above.
The combination does not explicitly teach a system comprising:
identifying one or more items of the set of items belonging to a category;
creating one or more groups of similar items of the set of items;
creating, a vector for the user and the set of items, wherein items of the set of items belong to a same group of the one or more groups;
when the vector for the item and the user cannot be estimated, selecting the vector equal to zero;
for the cluster in a cluster of users who purchase the item, identifying one or more percentile vectors for the user.
Notably, however, Carr does disclose using order history of a user to identify items purchases on a regular basis, including the purchase frequency (Carr: [0023]).
To that accord, Wang does teach a system comprising:
identifying one or more items of the set of items belonging to a category; (Wang: col. 11, ln. 27-64 – “process 200 for providing content recommendation, according to various embodiments of the present teaching. As shown, at 202, presence of a user may be detected, and a user profile as well as session information associated with the user may be obtained. The user profile obtained at 202 may include information indicating content features that have been viewed in the past by the user. For example, such content features in the user profile may include phrases, keywords, specific content identification (e.g., a title of the content item), content topics, content categories and/or any other content features that have been viewed by the user in the past. In some implementations, these features may be indexed in accordance with the frequencies at which the user has viewed them. As an illustration, a given user profile obtained at 202 may indicate that the use has viewed contents related to sports (e.g., a topic or category) most often in the past, content items related to finance second most often in the past and so on. As another illustration, the given user profile obtained at 202 may indicate the user has viewed content items containing keywords “LeBron James” most often in the past, and contents containing keywords “Cleveland city council” second most often, and so on. The user session information obtained at 202 may include information indicating one or more content items that have been viewed by the user in a session or sessions engaged by the user (e.g., the current session and/or a number of historical sessions). (39) At 204, a first set of candidate content items may be generated based on the user profile obtained at 202. The first set of candidate content items may be generated based on content items stored in the content database. The first set of candidate content items at 204 may be determined based on considerations, e.g., whether the content item relates to the user's interests according to the user profile. The interests of a user as recorded in the user profile may be determined based on, e.g., declared interests and/or interests that are inferred for the user based on, e.g., user activities observed”).
creating one or more groups of similar items of the set of items; (Wang: col. 11, ln. 27-64 – “process 200 for providing content recommendation, according to various embodiments of the present teaching. As shown, at 202, presence of a user may be detected, and a user profile as well as session information associated with the user may be obtained. The user profile obtained at 202 may include information indicating content features that have been viewed in the past by the user. For example, such content features in the user profile may include phrases, keywords, specific content identification (e.g., a title of the content item), content topics, content categories and/or any other content features that have been viewed by the user in the past. In some implementations, these features may be indexed in accordance with the frequencies at which the user has viewed them. As an illustration, a given user profile obtained at 202 may indicate that the use has viewed contents related to sports (e.g., a topic or category) most often in the past, content items related to finance second most often in the past and so on. As another illustration, the given user profile obtained at 202 may indicate the user has viewed content items containing keywords “LeBron James” most often in the past, and contents containing keywords “Cleveland city council” second most often, and so on. The user session information obtained at 202 may include information indicating one or more content items that have been viewed by the user in a session or sessions engaged by the user (e.g., the current session and/or a number of historical sessions). (39) At 204, a first set of candidate content items may be generated based on the user profile obtained at 202. The first set of candidate content items may be generated based on content items stored in the content database. The first set of candidate content items at 204 may be determined based on considerations, e.g., whether the content item relates to the user's interests according to the user profile. The interests of a user as recorded in the user profile may be determined based on, e.g., declared interests and/or interests that are inferred for the user based on, e.g., user activities observed
creating a vector for the user and the set of items, wherein items of the set of items belong to a same group of the one or more groups; (Wang: col. 22, ln. 13-38 – “Once the training data points have been mapped to the feature space(s) (user space where users are mapped to via user related feature vectors, content space in which content items are mapped to via content related feature vectors, and user-content space in which user-content cross feature vectors are mapped), the model learning module 1308 carries out the learning using such training data based on rules associated with the learning model to be trained, as well the parameters in connection with the learning model to be trained. As all the training data points have been mapped to the feature space, what may be learned include which users share similar interests and on what topics, content items, etc. What may also be learned may include what content items have caused more active involvement of the users who do or do not share similar interests. Such knowledge is learned by the model learning module 1308 and embedded in the learning model to be trained. Ultimately, the model learning module 1308 may identify how the training data is clustered in the high dimensional space and how the points clustered together may be related to each other and in what manner as well as how data distributed in different clusters may be distinguished based on what features. Such knowledge is captured in the learning process via the learning model and parameters associated therewith so that the learned models can be utilized, in the future, to determine what appropriate content items are to be selected for which user”).
when the vector for the item and the user cannot be estimated, selecting the vector equal to zero; (Wang: col. 21, ln. 51-col.22, ln. 12 – “Another exemplary type of feature vector that the model learning module 1308 may utilize, as discussed above, is user-content cross feature vector. Based on received information on a user and content items associated with the user, the model learning module 1308 may construct an integrated feature space that combine the user features with content features. For each user, it may be combined with different content items to generate different feature points in the user-content space. A user-content cross feature vector may also be generated by the model learning module 1308 based on each given situation involving a user and a content item. For example, a user-content cross feature vector may be constructed based on different combinations of {user_id, user_feature} and {content_id, content_features} so that the user-content cross feature vector may be constructed based on different ranges of those feature value combinations. In this case, depending on a specific user and a given content item, the model learning module 1308 may generate a user-content cross feature vector by assigning a “1” for the user-content feature range appropriate for the given situation and assigning “0”s for all other ranges. For example, if a user is in the age group of 30-40 corresponding to #4 age group and the given content item has a content topic falling in the #13 content category/topic. Such a user-content cross feature vector generated by the model learning module 1308 corresponds to a point in the user-content space. Points having a close distance in this user-content space may signify that those users share some common interests in content of certain topics”).
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 invention of the combination of Carr and Chen disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the identifying items of a category to create groups of similar items and create vectors of the user and the set of items, the vector equal to zero when the item and user cannot be estimated as taught by Wang. One of ordinary skill in the art would have been motivated to do so in order to proactively estimate items of interest to the user (Wang: col. 1, ln. 49-67).
The combination in view of Wang does not explicitly teach for the cluster in a cluster of users who purchase the item, identifying one or more percentile vectors for the user. Notably, however, Carr does disclose ranking historical items of the user to recommend (Carr: [0034]).
To that accord, Mukherjee does teach for the cluster in a cluster of users who purchase the item, identifying one or more percentile vectors for the user. (Mukherjee: [0065] – “the vectors can be preprocessed before determining the similarity between them. For example, in some embodiments, a variance stabilizing transformation can be applied to the vectors. In some embodiments, the percentile rank of each consuming entity can be calculated for each provisioning entity”).
Mukherjee teaches creating a vector corresponding to each consuming entity (Mukherjee: [0065]).
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 invention of the combination of Carr and Chen in view of Wang disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the identifying percentile vectors as taught by Mukherjee. One of ordinary skill in the art would have been motivated to do so in order to classify large sets of data to efficiently identify patterns (Mukherjee: [0002]).
Regarding Claim 17: Claim 17 recites substantially similar limitations as claim 7. Therefore, claim 17 is rejected under the same rationale as claim 7 above.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Carr (US 20160125506 A1) and Chen (US 20160125506 A1) in view of Ross (US 11,062,378 B1).
Regarding Claim 8: The combination of Carr and Chen discloses the limitations of claim 1 above.
Carr further discloses a method comprising:
determining, using a second set of rules, a MAXGAP, wherein the MAXGAP comprises: for the user and the item, the MAXGAP is a maximum number of times the user skipped replenishing the item based on a time between (k-1)-th replenishment and a k-th replenishment for the user as obtained from historical sales data of a retailer; (Carr: [0031] – “If the number of times a customer has declined to add an item exceeds a threshold value, that item may not be suggested to the customer. For example, if the customer last purchased toothpaste six weeks ago, the system will identify the number of times toothpaste has been suggested to the customer within the last six weeks. Any item suggestions presented to the user more than six weeks ago are not considered”).
when the user of the cluster of users has bought the item last from the retailer more than an N number of days before a predetermined day: determining that the user will no longer replenish the item; or removing the item from the personal replenishment cycle of the item for the user. Examiner notes that Applicant recites or in the claim. (Carr: [0032] – “The number of times a customer has declined to add an item to their order may be an indication that the customer no longer wants to purchase that item. For example, the customer may no longer like the item, may have selected a different type of item, may have selected a different brand of the item, may have stopped using the item, or may be purchasing the item from a different merchant. In some embodiments, method 500 may limit the number of times an item is suggested to a particular customer. For example, if a customer declines a suggestion to add toothpaste to their order four consecutive times, the system may no longer suggest toothpaste (e.g., that particular brand of toothpaste) to the customer”).
Carr does not explicitly teach for the item and a cluster of users, including the user, who purchased the item, determining a p-th percentile of the MAXGAP of the user of the cluster of users; Notably, however, Carr does disclose the number of times a user declined a suggestion to replenish an item (Carr: [0031]).
To that accord, Ross does teach for the item and a cluster of users, including the user, who purchased the item, determining a p-th percentile of the MAXGAP of the user of the cluster of users; (Ross: col. 10, ln. 63-col.11, ln. 12 – “a product purchase rank can include a raw product purchase score. In an embodiment, a higher product purchase score may indicate a higher probability of a successful cross sale. In another embodiment, a product purchase rank incudes a tier corresponding to a given product purchase score, wherein the tier is selected from a plurality of tiers that are based upon a distribution of product purchase scores for a population of customers of the enterprise. For example, “low”, “medium” and “high” tiers may represent different segments or tiers within the distribution of product purchase scores. In an embodiment, a product purchase rank includes a percentile classification of a given product purchase score relative to all product purchase scores for a population of customers of the enterprise. In an embodiment, a product purchase rank can include a combination of the above types of rank”).
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 invention of the combination of Carr and Chen disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the determining of a percentile of the MAXGAP of the user of the cluster of users as taught by Ross. One of ordinary skill in the art would have been motivated to do so in order to determine items with a higher probability of a successful sale (Ross: col. 10, ln. 65-66).
Regarding Claim 18: Claim 18 recites substantially similar limitations as claim 8. Therefore, claim 18 is rejected under the same rationale as claim 8 above.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Carr (US 20160125506 A1) and Chen (US 20160125506 A1), in view of Kowalchuk (US 20120053951 A1), and in further view of White (US 20130282626 A1).
Regarding Claim 9: The combination of Carr and Chen discloses the limitations of claim 1 above.
Carr further discloses a system comprising:
identifying a personalized list of recommended items to consider replenishing; (Carr: [0038] – “User interface 600 includes a listing of items 602 selected by the customer in the current order. As discussed herein, order management system 114 may suggest one or more items to the customer based on purchase frequency, last purchase date, and number of declined suggestions. Example suggested items are shown in a window 604 along with a notation “Have you forgotten these items?” In the example of FIG. 6, four suggested items are shown in window 604. The customer can select any number of the suggested items (e.g., Fruity Cereal is selected in window 604) and click the “Add Item To Order” button 606 to add the selected item to the current order”).
estimating a number of replenishments of the item that the user has made since the user last bought the item from the different retailer, wherein the number of replenishments of the item comprises the number of times the user has replenished the item; (Carr: [0031] – “determining the last date the customer purchased each of the identified items at 506. For example, the system may determine that the customer last purchased toothpaste six weeks ago and last purchased bananas 10 days ago. Method 500 also identifies at 508 a number of times the item has been suggested to the customer, but the customer declined to add the item to their order. In some embodiments, method 500 monitors the number of times a customer has declined to add an item to their order since the last time the customer purchased that item”). In summary, the purchases of the items are tracked in the order data, and the number of times the item has been replenished is merely the difference of the total number of times the item was suggested and the number of times it was declined.
removing from consideration any items from the set of items greater than a p-th percentile of MAXGAP for the item, where the user belongs to a cluster of users who purchase the item; (Carr: [0029] – “the number of top item suggestions is limited to a specific number of items (N), such as the top five or top seven items. In other embodiments, any number of item suggestions are presented to the customer. In alternate embodiments, only the top item suggestion is identified. Method 400 displays the top item suggestions (or suggestion) to the customer and queries the customer regarding whether to add any of the suggested items to the current order”).
Carr does not explicitly teach a system comprising:
identifying, using a third set of rules, a personalized list of recommended items for the user to consider replenishing, and a likelihood that a user of the computer has purchased the item from a different retailer;
modeling an elapsed time using at least different (user, item) pairs, wherein a (user, item) pair of at least different (user, item) pairs comprises independent random variables, wherein each independent random variable of the independent random variables comprises a respective expectation and a respective variance, and wherein the respective variance comprises a respective mean replenishment cycle based on the (user, item) pair corresponding to an estimated standard deviation;
estimating the independent random variables for the user and remaining items of the set of items for the user.
Notably, however, Carr does disclose where the customer may be purchasing the item from a different merchant (Carr: [0032]).
To that accord, Kowalchuk does teach identifying a personalized list of recommended items to consider replenishing, and a likelihood that a user of the computer has purchased the item from a different retailer; (Kowalchuk: [0021] – “The screening module is configured to determine a likelihood that a particular consumer will visit a particular retailer. For a traditional store, the visit is manifested as a physical presence at or in the traditional store. For a web-based store, the visit is manifested as accessing the web site associated with the web-based store. Thus, when the system 10 receives a list of potential consumers who have been identified as targeted prospects for purchasing a particular product, the screening module 18 may be utilized to help determine, for each consumer on the list, a likelihood that the consumer will purchase the particular product at the particular retailer”).
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 invention of the combination of Carr and Chen disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the likelihood a user purchased the item from a different retailer as taught by Kowalchuk. One of ordinary skill in the art would have been motivated to do so in order to target prospective consumers for purchasing products (Kowalchuk: [0021]).
The combination of Carr and Chen in view of Kowalchuk does not explicitly teach a system comprising:
modeling an elapsed time using at least different (user, item) pairs, wherein a (user, item) pair of at least different (user, item) pairs comprises independent random variables, wherein each independent random variable of the independent random variables comprises a respective expectation and a respective variance, and wherein the respective variance comprises a respective mean replenishment cycle based on the (user, item) pair corresponding to an estimated standard deviation;
estimating the independent random variables for the user and remaining items of the set of items for the user.
Notably, however, Carr does disclose ranking the regularly purchased item based on various criteria (Carr: [0034]), and determining a purchase frequency of items (Carr: [0030).
To that accord, White does teach a system comprising:
modeling an elapsed time using at least different (user, item) pairs, wherein a (user, item) pair of at least different (user, item) pairs comprises independent random variables, wherein each independent random variable of the independent random variables comprises a respective expectation and a respective variance, and wherein the respective variance comprises a respective mean replenishment cycle based on the (user, item) pair corresponding to an estimated standard deviation; (White: [0122] – “The experimental design is a subset of the entire attribute space with usually far fewer unique treatments than the complete attribute space. The creation of a design is intimately related to the attribute space under examination and the model form that is hypothesised to explain the phenomena under examination (through estimation of model parameters). Typically the attribute space is obtained from the problem definition (eg signature), but for a given attribute space there may be various appropriate experimental designs and models which may be used to obtain estimates of the model parameters (eg coefficients .beta.). For a given design, different models vary in how they perform estimation of model parameters and their variance, and thus different models have different strengths and weaknesses”).
estimating the independent random variables for the user and remaining items of the set of items for the user. (White: [0122] – “perform estimation of model parameters and their variance, and thus different models have different strengths and weaknesses”).
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 invention of the combination of Carr and Chen, in view of Kowalchuk disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the model based on independent random variables that are mutually independent and identically distributed, and estimating parameters as taught by White. One of ordinary skill in the art would have been motivated to do so in order to accurately predict human behavior (White: [0008]).
Regarding Claim 19: Claim 19 recites substantially similar limitations as claim 9. Therefore, claim 19 is rejected under the same rationale as claim 9 above.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Carr (US 20160125506 A1) and Chen (US 20160125506 A1), in view of Licht (US 20180204267 A1).
Regarding Claim 10: Carr discloses the limitations of claim 1 above.
Carr further discloses displaying, on the graphical user interface, item information for the item; (Carr: [0038] – “User interface 600 includes a listing of items 602 selected by the customer in the current order. As discussed herein, order management system 114 may suggest one or more items to the customer based on purchase frequency, last purchase date, and number of declined suggestions. Example suggested items are shown in a window 604 along with a notation “Have you forgotten these items?” In the example of FIG. 6, four suggested items are shown in window 604. The customer can select any number of the suggested items (e.g., Fruity Cereal is selected in window 604) and click the “Add Item To Order” button 606 to add the selected item to the current order”).
the historical sales data comprise a record indicating that the user has stopped purchasing the first item. (Carr: [0032] – “The number of times a customer has declined to add an item to their order may be an indication that the customer no longer wants to purchase that item. For example, the customer may no longer like the item, may have selected a different type of item, may have selected a different brand of the item, may have stopped using the item, or may be purchasing the item from a different merchant”).
Carr does not explicitly teach a system comprising:
displaying, on the graphical user interface and at a second time after the first time, a promotion for the item;
the second time is within a predetermined time period from completion of the personal replenishment cycle for the item that began at the first time;
historical sales data for the item comprises an estimated time period during for repurchasing of the item;
Notably, however, Carr does disclose identifying a frequency of how often a user purchases and item considering items within that time frame (Carr: [0031]).
To that accord, Licht does teach a system comprising:
displaying, on the graphical user interface and at a second time after the first time, a promotion for the item; (Licht: [0104] – “the inventory replenishment manager notifies the consumer of an offer for a different item made by a third-party based on a same type for the specific item and the different item but the different item provided by a different manufacturer than a manufacturer of the specific item. For example, if the specific item is green beans and the consumer typically buys brand X but the store has a special on brand Y, then the inventory replenishment manager suggest that the consumer try brand Y to enjoy a discount on the green beans”).
the second time is within a predetermined time period from completion of the personal replenishment cycle for the item that began at the first time; (Licht: [0103] – “the inventory replenishment manager engages the consumer for ordering a different item that is analyzed from the history to also be in need of replenishment within a configurable number of days beyond a date that the consumer is notified of the specific item. For example, if milk is the specific item but bananas are in need of replenishment a few days after the milk than the inventory replenishment manager suggests replenishment of the bananas with the milk”).
historical sales data for the item comprises an estimated time period during for repurchasing of the item; (Licht: [0094] – “the inventory replenishment manager predicts based on the analyzed history a specific item that is in need of replenishment by the consumer. This prediction provides a date in which it is likely that the consumer will need the item to replenish the item based on the consumer's consumption pattern with respect to the item”).
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 invention of the combination of Carr and Chen disclosing the system for identifying and suggesting items to the user to repurchase and determining when the user stopped purchasing the item with the displaying a promotion for the item, displaying item information within a predetermined time period, and estimating a time period for repurchasing the item as taught by Licht. One of ordinary skill in the art would have motivated to do so in order to identify other items in need of replenishment soon and identify discounts for similar products (Licht: [0103-0104]).
Regarding Claim 20: Claim 20 recites substantially similar limitations as claim 10. Therefore, claim 20 is rejected under the same rationale as claim 10 above.
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 TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 8:30am - 7:00pm EST.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 5/21/2026
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