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 .
Status of Claims
Claims 1-20 were amended in the response filed October 30, 2025.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Detailed rejections begin on page 3.
Response to Arguments begins on page 27.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The steps for determining eligibility under 35 U.S.C. 101 can be found in the MPEP § 2106.03-2106.05.
Under Step 1, the claims are directed to statutory categories. Specifically, the method, as claimed in claims 1-13, is directed to a process. Additionally, the computing system, as claimed in claims 14-19, is directed to a machine. Furthermore, the one or more non-transitory computer-readable media, as claimed in claim 20, is directed to an article of manufacture.
While the claims fall within statutory categories, under Step 2A, Prong 1, the claimed invention recites the abstract idea of product recommendation. Specifically, representative claim 1 recites the abstract idea of:
accessing request data indicative of an item associated with a user;
accessing preference data associated with the user, wherein the preference data is indicative of a fattiness level of the item;
obtaining data indicative of a plurality of items that are available at a physical location;
computing a recommended item from the plurality of items available at the physical location based on preference data;
process input data comprising the request data, the preference data, and the sensor data to compute the recommended item, and
output data indicative of the recommended item; and
indicates the recommended item.
Under Step 2A, Prong 1, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in the guidance. When considering MPEP §2106.04(a), the claims recite an abstract idea. For example, representative claim 1 recites the abstract idea of product recommendations, as noted above. This concept is considered to be a certain method of organizing human activity. Certain methods of organizing human activity are defined in the MPEP as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP §2106.04(a)(2) subsection II. In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because accessing request data indicative of an item associated with a user; accessing preference data associated with the user, wherein the preference data is indicative of a fattiness level of the item; obtaining data indicative of a plurality of items that are available at a physical location; computing a recommended item from the plurality of items available at the physical location based on preference data; process input data comprising the request data, the preference data, and the sensor data to compute the recommended item; output data indicative of the recommended item; and indicates the recommended item are sales activities. Thus, representative claim 1 recites an abstract idea.
The recited limitations of representative claim 1 also recite an abstract idea because they are considered to be mental processes. As described in the MPEP, mental processes are “concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”. MPEP §2106.04(a)(2) subsection III. In this case, accessing request data indicative of an item associated with a user; accessing preference data associated with the user, wherein the preference data is indicative of a fattiness level of the item; and indicates the recommended item are types of judgement. Additionally, obtaining data indicative of a plurality of items that are available at a physical location is a type of observation. Furthermore, computing a recommended item from the plurality of items available at the physical location based on preference data; process input data comprising the request data, the preference data, and the sensor data to compute the recommended item; and output data indicative of the recommended item are types of evaluation. Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. See MPEP §2106.04(d). In this case, representative claim 1 includes additional elements such as a computer, a mobile computing device; one or more sensors of the mobile user device, sensor data; one or more machine-learned models, wherein the one or more machine-learned models are trained using training data and a loss function that backpropagates errors associated with the training data to update at least one parameter of the one or more machine learning models according to a generalization technique; and generating, by the mobile computing device, a user interface.
Although reciting additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., product recommendations) being applied on a general purpose computer. See MPEP §§2106.04(d) and 2106.05(f). Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application. As such, representative claim 1 is directed to an abstract idea.
Under Step 2B, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). See MPEP §2106.05.
In this case, as noted above, the additional elements recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components ... ‘ad[d] nothing ... that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014). (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Also see MPEP §2106.05(f). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 1 is ineligible.
Dependent claims 2-13 do not aid in the eligibility of independent claim 1. For example, claims 2-7, 9-10, and 12-13 merely further define the abstract limitations of claim 1. Also, claims 8 and 11 merely provide further embellishments of the abstract limitations recited in independent claim 1.
Additionally, it is noted that claims 2-3, 5-6, and 8-11 and do not include further additional elements not previously recited. Therefore, the claims do not integrate the abstract idea into a practical application because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea. The claims also do not amount to significantly more than the abstract idea because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Furthermore, it is noted that claim 4 includes further additional elements of second sensor data; claim 7 includes further additional elements of wherein the one or more machine-learned models are retrained; claim 12 includes further additional elements of a memory; and claim 13 includes further additional elements of an item detection model and an item recommendation model wherein: the item detection model is trained; and the item recommendation model is trained. However, these additional elements do not integrate the abstract idea into a practical application because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea. These additional elements are merely generic elements and are likewise described in a generic manner in Applicant’s specification. Additionally, the additional elements do not amount to significantly more because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Thus, dependent claims 2-13 are also ineligible.
Lastly, the analysis above applies to all statutory categories of invention. Although literally invoking a machine and article of manufacture, respectively, claims 14-19 and 20 remain only broadly and generally defined, with the claimed functionality paralleling that of claims 1-2, 4-7; and claim 1, respectively. It is noted that claim 14 includes further additional elements of a computing system comprising: one or more processors; and one or more non-transitory, computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations, and claim 20 includes further additional elements of one or more non-transitory computer-readable media storing instructions that are executable to cause one or more processors to perform operations. However, these additional elements do not integrate the abstract idea into a practical application because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea. These additional elements are merely generic elements and are likewise described in a generic manner in Applicant’s specification. Additionally, the additional elements do not amount to significantly more because they merely amount to an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea. As such, claims 14-19 and 20 are rejected for at least similar rationale as discussed above for claims 1-2, 4-7; and claim 1, respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnan Gorumkonda (US 9824298 B1, herein referred to as Krishnan), in view of van Horn et. al. (US 11443362 B2, herein referred to van Horn), in further view of Bae et. al. (US 20230267704 A1, herein referred to as Bae).
Claim 1:
Krishnan discloses:
A computer-implemented method comprising {Krishnan: [Col. 20, ln. 22-23] methods and for predicting and detecting produce quality are described}:
accessing, by a computing device, request data indicative of an item {Krishnan: [Col. 10, ln. 28-30] item analytics computers 210 may receive a communication from a user for a desired produce item.},
accessing, by the computing device, preference data associated with the user {Krishnan: [Col. 10, ln. 28-31] item analytics computers may receive a communication from a user for a desired produce item associated with a desired ripeness score.};
obtaining, via one or more sensors of the computing device, sensor data indicative of a plurality of items that are available at a physical location {Krishnan: [Col. 9, ln. 36-42] item analytics computers 210 may scan a barcode or other identifier associated with the produce item (e.g., at a crate that stores the produce item, a barcode on a sticker affixed to the produce item, etc.). The barcode or other identifier may allow the one or more item analytics computers 210 to access information about the produce item (e.g., is it an apple, pear, avocado, etc.); [Col. 9, ln. 64-67; Col. 10, ln. 1] data representing visual or infrared characteristics of a produce item may be received. For example, the item analytics computers 210 may receive visual characteristics associated with the produce item from one or more imaging devices or cameras; [Col. 4, ln. 21-22] a corresponding produce item at a facility (e.g., physical location)};
computing, by the computing device using one or more machine-learned models, a recommended item from the plurality of items that are available at the physical location for selection based preference data {Krishnan: [Col. 2, ln. 24-27] camera(s) including infrared (IR) images, digital (red/green/blue or RGB) images, etc.) to identify other characteristics of the apples; [Col. 9, ln. 49-52] A ripeness regression (i.e., machine learning model) may also be identified from the training data set and/or ripeness characteristics of the produce item. The ripeness characteristics can represent a ripeness regression of the produce type over a time frame; [Col. 10, ln. 55-63] item analytics computers 210 may instruct that the desired produce item be provided to the user upon determining that the ripeness score of the produce item matches the desired ripeness score requested by the user.},
wherein the one or more machine-learned models are trained using training data to update at least one parameter of the one or more machine-learned models {Krishnan: [Col. 3, ln. 4-6] a training data set may be generated that can help predict a ripeness regression for a future produce item of a same or similar produce type or variety; [Col. 8, ln. 59-64] feedback module 246 may also be configured to update item data (e.g., via training data module 238) to update the ripeness regression (e.g., via the regression module 240) to identify the ripeness of the produce item at a particular date.}, and
wherein the one or more machine-learned models are trained to {Krishnan: [Col. 3, ln. 53-55] observed ripeness regressions may be observed and/or analyzed in association with previously-identified training data sets}:
process input data comprising the request data, the preference data, and the sensor data to compute the recommended item {Krishnan: [Col. 10, ln. 20-23] item analytics computers 210 may determine a ripeness score of the produce item based at least in part on a comparison of the data representing visual characteristics and the training data set; [Col. 4, ln. 3-7] user 122 may submit a request for a produce item associated with a particular ripeness, which is received by computing device 104. Network page 124 may also provide information (e.g., visual characteristics, infrared characteristics, images, item data, etc.); [Col. 8, ln. 18-29] regression module 240 may be configured to associate ripeness characteristics of a produce type of the produce item with a ripeness score (e.g., by a particular date, based in part on environmental factors, or other item data). Based in part on the ripeness characteristics of the produce item, ripeness characteristics of other similar produce items, a time frame, one or more environmental factors, the estimated ripeness when the produce item arrives at a user's location, etc., similar items that are analyzed after the initial item (e.g., a second item) may be compared with initial item to help predict how ripe the second item will be in the future and at what date a desired ripeness can be achieved.}, and
output data indicative of the recommended item {Krishnan: [Col. 10, ln. 56-60] item analytics computers 210 may instruct that the desired produce item be provided to the user upon determining that the ripeness score of the produce item matches the desired ripeness score requested by the user.}; and
generating, by the computing device, a user interface that indicates the recommended item {Krishnan: fig 9, interface 924 of user device 922; [Col. 19, ln. 63-67; Col. 20, ln. 1-5] computing device 904 can provide a communication to the user with a recommendation of how to store the produce and/or achieve the desired ripeness score in the desired time frame. As illustrated, the recommendation includes “Your apples are on their way! We recommend that you store them in a refrigerator due to the humidity in your area at this time of year.”}.
Although disclosing a method for recommending grocery items, Krishnan does not disclose:
accessing, by a mobile computing device, request data indicative of an item,
accessing, by the mobile computing device, preference data associated with the user;
obtaining, via one or more sensors of the mobile computing device, sensor data indicative of a plurality of items that are available at a physical location;
computing, by the mobile computing device, a recommended item, and
generating, by the mobile computing device, a user interface that indicates the recommended item.
Krishnan does disclose that user devices can be connected to the store computing device (fig 1, user device 122 in communication with store computing device 104).
However, van Horn teaches:
accessing, by a mobile computing device, request data indicative of an item {van Horn: fig 1, picker application 112; fig 4A; [Col. 6, ln. 25-26] picker 108 accesses the PMA 112 via a mobile client device, such as a mobile phone or tablet; [Col. 7, ln. 11-21] A picker 108 may use the picker order interface 400A when gathering items 410 for an order 405 at a retailer 110. The picker order interface 400A displays a scrollable list of items 410 in the order 405. The scrollable list may include other information about the items 410, such as name of the customer 104 associated with the order 405.};
accessing, by the mobile computing device, preference data associated with the user {van Horn: [Col. 6, ln. 25-26] picker 108 accesses the PMA 112 via a mobile client device, such as a mobile phone or tablet; [Col. 7, ln. 11-21] picker order interface 400A displays a scrollable list of items 410 in the order 405, including the brand or type of the item, the quantity ordered, the price of the item, and an image of the item.};
obtaining, via one or more sensors of the mobile computing device, sensor data indicative of a plurality of items that are available at a physical location {van Horn: [Col. 6, ln. 25-26] picker 108 accesses the PMA 112 via a mobile client device, such as a mobile phone or tablet; [Col. 8, ln. 1-5] picker 108 may interact with the camera icon 430 to take a picture of replacement options or to show the quality of the item available at the retailer 110 and send the picture to the customer 104 along with the message.};
computing, by the mobile computing device, a recommended item {van Horn: [Col. 6, ln. 25-26] picker 108 accesses the PMA 112 via a mobile client device, such as a mobile phone or tablet; [Col. 4, ln. 44-45] order fulfillment engine 206 only uses data for the customer 104 related to the order to suggest replacement options.}, and
generating, by the mobile computing device, a user interface that indicates the recommended item {van Horn: [Col. 6, ln. 25-26] picker 108 accesses the PMA 112 via a mobile client device, such as a mobile phone or tablet; [Col. 7, ln. 36-44] picker 108 may interact with the “Can't Find Item” 420 button to request a desired course of action for the item 410 from the customer 104. The picker 108 may also interact with the “Ask a question” 425 button to send a custom message to the customer 104; [Col. 7, ln. 48-50] picker order interface 400C is displayed after the picker 108 interacts with the “Can't Find Item” 420 button from FIG. 4B; [Col. 9, ln. 1-5] picker 108 customer 104 may continue to message back and forth using the picker order interface 400 and the customer ordering interface 500, respectfully, to resolve any other issues with the order 505.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included a mobile device used by a personal shopper as taught by van Horn in the product quality detection and prediction method of Krishnan in order to address any issues that arise with a customer order (van Horn: [Col. 1, ln. 14-23]).
Neither Krishnan nor van Horn disclose:
wherein the preference data is indicative of a fattiness level of the item; and
wherein the one or more machine-learned models are trained using training data and a loss function that backpropagates errors associated with the training data to update at least one parameter of the one or more machine-learned models according to a generalization technique.
Krishnan does disclose that a user can submit a request for a produce item with their preferences for the item (Krishnan: [Col. 10, ln. 28-31]) and that a machine learning model can be trained to update parameters (Krishnan: [Col. 8, ln. 59-64]).
However, Bae teaches:
preference data is indicative of a fattiness level of the item {Bae: [0082] in the case of the sirloin part, the grade of the individual is measured by measuring the degree of intermuscular fat distribution (marbling); [0121] server 100 may highlight and display on each user terminal packed meats of the quality classification determined as preferred by each user using each user terminal}; and
wherein the one or more machine-learned models are trained using training data and a loss function that backpropagates errors associated with the training data to update at least one parameter of the one or more machine-learned models according to a generalization technique {Bae: [0140] artificial intelligence training, a data set including the purchase histories of packed meats by each meat, each part, and each grade of consumers for a predefined period may be acquired as each training data; [0147] A process of comparing the output of the artificial intelligence corresponding to inference and the label corresponding to a correct answer may be performed by calculating a loss function; [0148] learning device may optimize the artificial intelligence based on a comparison value (650). By updating weights of nodes of the artificial intelligence so that the learning device comparison value becomes smaller and smaller, the output of the artificial intelligence corresponding to the inference and the label corresponding to the correct answer can be gradually matched. For the optimization of artificial intelligence, a known backpropagation algorithm, stochastic gradient descent (i.e., generalization technique) may be used}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the machine learning methods for determining fattiness of meat as taught by Bae in the product quality detection and prediction method of Krishnan and van Horn in order to implement a technology for selling packed meat, etc. on the Internet based on an “As is image” that is a photographed image of fresh food to be actually delivered to a consumer in a web page or application (Bae: [0006]).
Claim 2:
Krishnan, van Horn, and Bae teach the method of claim 1. Krishnan further discloses:
wherein the one or more machine-learned models are trained to:
obtain a previous request for the item, wherein the previous request for the item indicates a previous preference of the user {Krishnan: [Col. 8, ln. 36-38] request module 242 may identify the request without a communication from a user (e.g., based on an identified pattern in a user's order history); [Col. 10, ln. 34-41] user may select the desired ripeness score based in part on images of produce items associated with the ripeness score. In some examples, the ripeness score of the produce item is determined from a passive communication (e.g., shopping/order history).}; and
determine the preference data of the user based on the previous request for the item {Krishnan: [Col. 19, ln. 50-57] computing device 904 can determine a future need for the produce item based in part on the history of produce items requested (e.g., the prediction can include a request from the user for bananas next Thursday associated with the ripeness score of 7). The computing device may provide communication associated with future need for the produce item that matches at least one desired ripeness score requested by the user}.
Claim 3:
Krishnan, van Horn, and Bae teach the method of claim 1. Krishnan further discloses:
wherein the one or more machine-learned models are trained to compute the recommended item by:
identifying an item from the plurality of items currently available at the physical location, wherein the identified item is indicative of an individual or grouping of items {Krishnan: [Col. 2, ln. 19-28] a crate of apples is received at a fulfillment center. The system checks the apples for firmness utilizing one or more sensors (e.g., imaging devices, cameras, etc.), and data generated by the sensors (e.g., captured images from imaging device(s) or camera(s) including infrared (IR) images, digital (red/green/blue or RGB) images, subsurface scattering, etc.) to identify other characteristics of the apples.};
analyzing the identified item to determine characteristics, wherein the characteristics are associated with the preference data {Krishnan: [Col. 2, ln. 27-30] system compares the data utilized to identify visual and/or infrared characteristics with other apples that were analyzed earlier in order to determine the relative ripeness of each apple of the crate of apples.}; and
determining the recommended item based on the characteristics of the item {Krishnan: [Col. 2, ln. 30-38] . Apples from the crate and other crates are then offered to users to order through a network page. When the user requests apples at the ripeness level that matches the ripeness level of at least some of the apples from the crate of apples, apples from the crate can be shipped to the user. If the user wants less ripe apples, apples from another crate with corresponding less ripe apples may be shipped to the user}.
Claim 4:
Krishnan, van Horn, and Bae teach the method of claim 1, including the steps being performed by a mobile computing device (van Horn: [Col. 6, ln. 25-26]). Krishnan further discloses:
obtaining, via the one or more sensors of the computing device, second sensor data, wherein the second sensor data is indicative of the recommended item {Krishnan: [Col. 9, ln. 64-67; Col. 10, ln. 1] data representing visual (first sensor data) or infrared (second sensor data) characteristics of a produce item may be received. For example, the item analytics computers 210 may receive visual characteristics associated with the produce item from one or more imaging devices or cameras; [Col. 2, ln. 24-26] camera(s) including infrared (IR) images (i.e., second sensor data), digital (red/green/blue or RGB) images (i.e., first sensor data), etc.)}; and
determining, by the computing device and using the one or more machine-learned models, the recommended item based on the second sensor data and the preference data {Krishnan: [Col. 2, ln. 24-27] camera(s) including infrared (IR) images, digital (red/green/blue or RGB) images, etc.) to identify other characteristics of the apples; [Col. 9, ln. 49-52] A ripeness regression (i.e., machine learning model) may also be identified from the training data set and/or ripeness characteristics of the produce item. The ripeness characteristics can represent a ripeness regression of the produce type over a time frame}.
Claim 5:
Krishnan, van Horn, and Bae teach the method of claim 1, including the steps being performed by a mobile computing device (van Horn: [Col. 6, ln. 25-26]). Krishnan further discloses:
accessing, by the computing device, data indicative of the one or more machine-learned models based on a type of the item {Krishnan: [Col. 18, ln. 23-30] ripeness regression may vary based in part on item data. For example, the produce type may be determined as organic. The ripeness regression may be altered based in part on the identification. In some examples, a different ripeness regression may correspond with the produce item, such that the ripeness regression associated with organic produce is accessed instead of the ripeness regression associated with another origin of the produce item}.
Claim 6:
Krishnan, van Horn, and Bae teach the method of claim 1, including the steps being performed by a mobile computing device (van Horn: [Col. 6, ln. 25-26]). Krishnan further discloses:
accessing, by the device, data indicative of the one or more machine-learned models based on the user associated with the item {Krishnan: [Col. 19, ln. 54-57] computing device 904 may provide a communication associated with the future need for the produce item that matches the at least one desired ripeness score requested by the user}.
Claim 7:
Krishnan, van Horn, and Bae teach the method of claim 1. Krishnan further discloses:
wherein the one or more machine-learned models are retrained based on feedback data from the user {Krishnan: [Col. 20, ln. 6-17] user may provide feedback as well. For example, after the user receives the item corresponding with the desired ripeness score, the user may communicate with the computing device 904 regarding the received ripeness of the produce item(s). This may include images of the produce item at arrival, environmental factors (e.g., the amount of sunlight around the produce item for storage, etc.), or other feedback. Based in part on the feedback, a received ripeness score may be determined for the produce item (e.g., predicted ripeness score of 5, received ripeness score of 8, etc.). In some examples, the feedback may be used to update the ripeness regression(s) as well.},
wherein the feedback data is indicative of a satisfaction of the user with the recommended item {Krishnan: [Col. 20, ln. 13-15] Based in part on the feedback, a received ripeness score may be determined for the produce item (e.g., predicted ripeness score of 5, received ripeness score of 8, etc.); [Col. 2, ln. 60-63] feedback (e.g., communication(s) from the user identifying the received ripeness when the produce item arrived at a location associated with the user, including “This was too ripe when I needed it!” or “Perfect! Thanks!”)}.
Claim 8:
Krishnan, van Horn, and Bae teach the method of claim 1. Krishnan further discloses:
wherein the preference data associated with the item is indicative of at least one of a ripeness level {Krishnan: [Col. 4, ln. 3-4] user 122 may submit a request for a produce item associated with a particular ripeness}.
Claim 9:
Krishnan, van Horn, and Bae teach the method of claim 1, including the steps being performed by a mobile computing device (van Horn: [Col. 6, ln. 25-26]). Krishnan further discloses:
generating, by the computing device and based on a selection of the recommended item for the item, the user interface that indicates the item has been addressed {Krishnan: fig 9, interface 924 updating for user on their device 922 to indicate the produce is on its way; [Col. 19, ln. 63-67; Col. 20, ln. 1-5] computing device 904 can provide a communication to the user with a recommendation of how to store the produce and/or achieve the desired ripeness score in the desired time frame. As illustrated, the recommendation includes “Your apples are on their way! We recommend that you store them in a refrigerator due to the humidity in your area at this time of year.”}.
Claim 10:
Krishnan, van Horn, and Bae teach the method of claim 1, including the steps being performed by a mobile computing device (van Horn: [Col. 6, ln. 25-26]). Krishnan further discloses:
determining, by the computing device, that the item is not currently available at the physical location {Krishnan: [Col. 2, ln. 32-41] When the user requests apples at the ripeness level that matches the ripeness level of at least some of the apples from the crate of apples, apples from the crate can be shipped to the user. If the user wants less ripe apples, the user may be informed that the apples at the particular ripeness are unavailable (e.g., via a communication from the system).}; and
wherein the recommended item is a replacement item for the item {Krishnan: [Col. 2, ln. 32-41] the user may be informed that the apples at the particular ripeness are unavailable (e.g., via a communication from the system). If the user wants more ripe apples, different apples may be found}.
Claim 11:
Krishnan, van Horn, and Bae teach the method of claim 1. Krishnan further discloses:
wherein the preference data is generated based on user input provided by the user during formation of a delivery request {Krishnan: [Col. 17, ln. 8-14] user may access the calendar to identify Saturday morning as the desired date to receive the produce item. The calendar 612 may accept more than one date as date data (e.g., the desired ship date); [Col. 4, ln. 35-37] computing device 104 may instruct that the produce item that corresponds to the ripeness score identified by the user be provided to the user (e.g., shipped, ordered)}.
Claim 12:
Krishnan, van Horn, and Bae teach the method of claim 1. Krishnan further discloses:
wherein the preference data is accessed via a data structure stored in a memory, the data structure storing the preference data associated with the user over a plurality of delivery request instances {Krishnan: fig 2, memory 218 has data store 234; fig 10, data stores include user information 1016; [Col. 9, ln. 9-11] contents of the memory may include one or more data stores 234; [Col. 21, ln. 17-21] data store 1010 can include several separate data tables, databases or other data storage mechanisms and media for storing data relating to a particular aspect. For example, the data store illustrated includes mechanisms for storing user information 1016; [Col. 8, ln. 36-38] request module 242 may identify the request without a communication from a user (e.g., based on an identified pattern in a user's order history)}.
Claim 13:
Krishnan, van Horn, and Bae teach the method of claim 1. Krishnan further discloses:
wherein the one or more machine-learned models comprises:
an item detection model and an item recommendation model {Krishnan: fig 2, item data module 236, feedback module 246} wherein:
the item detection model is trained to receive the sensor data indicative of the plurality of items currently available for selection at the physical location, and in response to receipt of the sensor data, generate item data comprising at least: (i) a type of the items; and (ii) a quantity of items of the plurality of items {Krishnan: fig 2, item data module 236; [Col. 7, ln. 55-59] item data module 236 may be configured to determine quantitative and/or qualitative features of a produce item using one or more imaging devices, cameras, sensors to identify item data; [Col. 18, ln. 23-30] ripeness regression may vary based in part on item data. For example, the produce type may be determined as organic. The ripeness regression may be altered based in part on the identification. In some examples, a different ripeness regression may correspond with the produce item, such that the ripeness regression associated with organic produce is accessed instead of the ripeness regression associated with another origin of the produce item; [Col. 8, ln. 4-16] training module 238 may be configured to analyze the item data for an initial produce item and/or generate a training data set for a produce type. The training module may also be configured to interact with the regression module 240 to help determine a regression ripeness for the produce type, produce item, and/or based in part on visual characteristics of the produce item and/or training data set}; and
the item recommendation model is trained to receive the item data and the input data based on the preference data, and in response to receipt of the item data and input data, determine the recommended item from the plurality of items {Krishnan: fig 2, feedback module 246; [Col. 8, ln. 56-64] user may confirm the ripeness of the produce item when it is received with their desired ripeness score, or provide additional item data regarding the ripeness of the item at arrival. The feedback module 246 may also be configured to update item data (e.g., via the item data module 236 or training data module 238) to account for the additional information from the user and/or update the ripeness regression to identify the ripeness of the produce item at a particular date; [Col. 10, ln. 56-60] item analytics computers 210 may instruct that the desired produce item be provided to the user upon determining that the ripeness score of the produce item matches the desired ripeness score requested by the user.}.
Regarding claims 14-20, claims 14-19 are directed to a computer system, while claim 20 is directed to one or more non-transitory computer-readable media. Claims 14-19; and 20 recite limitations that are parallel in nature to those addressed above for claims 1-2 and 4-7; and 1, respectively which are directed towards a method. Therefore, claims 14-19; and 20 are rejected for the same reasons as set forth above for claims 1-2 and 4-7; and 1, respectively.
It is noted that claim 14 includes additional elements of:
A computing system comprising:
one or more processors; and
one or more non-transitory, computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations.
Krishnan discloses:
A computing system comprising {Krishnan: fig 2; [Col. 4, ln. 42-45] architecture that includes an item analytics computer, item data computer, and/or a user device connected via one or more networks}:
one or more processors {Krishnan: fig 2, processor(s) 224}; and
one or more non-transitory, computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations {Krishnan: fig 2, memory 218; [Col. 6, ln. 58-64] memory 218 may store program instructions that are loadable and executable on the processor(s) 224, as well as data generated during the execution of these programs. Depending on the configuration and type of item analytics computers 210, the memory 218 may be volatile (such as RAM) and/or non-volatile (such as ROM, flash memory, etc.)}.
It is also noted that claim 20 includes additional elements of:
One or more non-transitory computer-readable media storing instructions that are executable to cause one or more processors to perform operations.
Krishnan discloses:
One or more non-transitory computer-readable media storing instructions that are executable to cause one or more processors to perform operations {Krishnan: fig 2, memory 218; [Col. 6, ln. 58-64] memory 218 may store program instructions that are loadable and executable on the processor(s) 224, as well as data generated during the execution of these programs. Depending on the configuration and type of item analytics computers, the memory 218 may be volatile (such as RAM) or non-volatile (such as ROM, flash memory, etc.)}.
Response to Arguments
With respect to the rejections under 35 U.S.C. 101, Applicant’s arguments have been considered but are not persuasive because the claims still recite abstract ideas, namely sales activities (i.e., certain methods of organizing human activity) and mental processes, merely being applied to a generic computer. However, in view of the amendments, new grounds of rejection have been applied. These new grounds of rejection have been necessitated by Applicant’s amendments.
With respect to the rejections under 35 U.S.C. 103, Applicant’s arguments have been considered and are persuasive with respect to Krishnan and van Horn because neither of these references disclose preference data including a fattiness level nor using a loss function that backpropagates and a gradient technique for the one or more machine-learned models. However, in view of the amendments, new grounds of rejection have been applied. These new grounds of rejection have been necessitated by Applicant’s amendments.
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.
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/KATHERINE A BARLOW/Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 2/2/2026