DETAILED ACTION
The following is a non-final, first office action in response to the application filed July 2, 2024. Claims 1-20 are currently pending and have been examined.
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 .
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 (abstract idea) without significantly more.
Step 1: Statutory Category
(MPEP § 2106)
Claims 1-20 are directed towards a method, a computer-readable medium, and a system. The claims are directed to a statutory category: a process, and an article of manufacture, a machine as defined under 35 U.S.C. § 101.
Regarding Claim 1:
Step 2A, Prong One: Judicial Exception – Abstract Idea
(MPEP § 2106.04)
The claim recites limitations directed to collecting information, analyzing the information using a machine-learning model, and generating results to guide actions, which fall within recognized abstract idea categories.
The following limitations collectively recite the abstract idea:
receiving data with information about an item
accessing a machine-learning model trained to predict a likelihood of not finding the item
applying the model to output a findability score for the item
generating action signals based on the findability score
communicating the action signals to devices associated with pickers, a source, or users
These limitations describe analyzing item data to determine the likelihood that an item can be located and recommending actions based on that determination.
The limitations fall within the following groupings of abstract ideas identified in the 2019 Revised Patent Subject Matter Eligibility Guidance:
Mental processes
The steps of evaluating item availability and determining actions based on that evaluation involve observation, evaluation, and judgment, which could be performed mentally or with pen and paper.
Certain methods of organizing human activity
The claim also involves managing retail shopping and fulfillment activities, including directing pickers, store operators, and users regarding item retrieval and ordering.
Step 2A, Prong Two: Integration into a Practical Application
(MPEP § 2106.04(d))
Next, determine whether the additional elements integrate the abstract idea into a practical application.
Additional elements beyond the abstract idea
The claim recites:
a computer system comprising a processor and computer-readable medium
communication via a network with multiple devices
application of a machine-learning model
generation and communication of action signals
These elements represent generic computer components performing their conventional functions.
The claim does not recite:
an improvement to computer functionality
a specific improvement to machine-learning technology
a specific algorithm or architecture for the model
improvements to network operation or data storage.
Instead, the computer system merely performs the abstract idea of analyzing item data and communicating results.
The machine-learning model is described functionally, as being trained to predict findability, without specifying how the model improves computer technology or machine-learning techniques.
Step 2B: Inventive Concept
(MPEP § 2106.05)
Step 2B determines whether the claim includes additional elements that amount to significantly more than the abstract idea.
Additional elements
The claim recites:
processor
computer-readable medium
network communication
devices associated with users, pickers, and stores
These components are well-understood, routine, and conventional in networked computing environments.
The use of a machine-learning model to analyze retail data was also widely known prior to the effective filing date and is described at a high level of abstraction without specific implementation details.
The claim does not recite:
a novel ML architecture
a particular training method
a specific data structure
a technical improvement to computer systems.
Instead, the machine-learning model is invoked simply as a tool for predicting item availability in a retail environment.
Therefore, the claim is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding Claim 14 and 20
Independent claims 14 and 20 are parallel in scope to claim 1 and ineligible for similar reasons.
Regarding Claims 2-13 and 15-19
Dependent claims 2-13 and 15-19 merely set forth further embellishments to the abstract idea, and therefore do not confer eligibility on the claimed invention and are ineligible for similar reasons to claim 1.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-10 and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rao et al (US 2023/0113122 A1).
Regarding claims 1, 14, and 20, Rao discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
receiving, via a network from at least one of one or more devices of one or more pickers associated with an online system, a device of a source associated with the online system, one or more devices associated with one or more users of the online system, or a computing system associated with a physical receptacle utilized by at least one user of the online system for shopping in a location of the source, data with information about an item (Rao: Figure 4 - receive a delivery order comprising a set of items 402);
accessing a findability prediction machine-learning model of the online system, wherein the findability prediction machine-learning model is trained to predict a findability of the item representing a likelihood of not finding the item given that the item is actually available; applying the findability prediction machine-learning model to output, based at least in part on the received data, a findability score for the item that is indicative of the findability of the item (Rao: Figure 4 - retrieve a machine learned model that predicts a probability that an item is available at the warehouse 406);
generating, based on the findability score, one or more action signals for triggering one or more automated actions to enhance the findability of the item (Rao: Figure 4 - generate an instruction to a picker based on the probability 410);
communicating, via the network, the one or more action signals to at least one of a device of a picker associated with the online system, the device of the source, or a device associated with a user of the online system, the one or more action signals further prompting one or more actions by at least one of the picker, the source, or the user in relation to the item (Rao: Figure 4 - transmit the instruction to a mobile device of the picker).
Regarding claim 2, Rao discloses all of the limitations as noted above in claim 1. Rao further discloses wherein receiving the data comprises: receiving, from the one or more devices of the one or more pickers via the network, the data including one or more picker signals indicating that the item cannot be found in a location of the source (Rao: Figure 6 - instruct a picker to stop looking 610).
Regarding claim 3, Rao discloses all of the limitations as noted above in claim 1. Rao further discloses wherein receiving the data comprises: receiving, from the device of the source via the network, the data including one or more source signals with information about at least one of inventory of the item in a location of the source or transactions associated with the item in the location of the source over a defined time period (Rao: Figure 4 - predict, using the model, the probability that one of the set of items in the delivery order is available at the warehouse 408).
Regarding claims 4 and 16, Rao discloses all of the limitations as noted above in claims 1 and 14. Rao further discloses wherein receiving the data comprises: gathering, via one or more sensors mounted to the physical receptacle, at least one of scanning data with information about the item, one or more images of the item, or video data associated with the item; and receiving, from the computing system associated with the physical receptacle via the network, the data including at least one of the scanning data, the one or more images, or the video data (Rao: paragraph [0033] - The PMA 112 also includes an image encoder 326 which encodes the contents of a basket into an image. For example, the image encoder 326 may encode a basket of goods (with an identification of each item) into a QR code which can then be scanned by an employee of the warehouse 110 at check-out).
Regarding claim 5, Rao discloses all of the limitations as noted above in claim 1. Rao further discloses wherein receiving the data comprises: receiving, via the network from at least one of the one or more devices of the one or more pickers or the one or more devices associated with the one or more users, one or more item signals with information about whether the item was found by the one or more pickers or the one or more users; and generating, based on the one or more item signals, the data including information about a found rate for the item (Rao: paragraph [0018] - for example, for each item-warehouse combination (a particular item at a particular warehouse), the inventory database 204 may store a time that the item was last found, a time that the item was last not found (a picker looked for the item but could not find it), the rate at which the item is found, and the popularity of the item).
Regarding claim 6, Rao discloses all of the limitations as noted above in claim 1. Rao further discloses wherein receiving the data comprises: receiving, via the network from the one or more devices associated with the one or more users, one or more item signals with information about one or more in-store lists of an application of the online system running on the one or more devices associated with the one or more users; and generating, based on the one or more item signals, the data including information about a found rate for the item (Rao: paragraph [0018] - for example, for each item-warehouse combination (a particular item at a particular warehouse), the inventory database 204 may store a time that the item was last found, a time that the item was last not found (a picker looked for the item but could not find it), the rate at which the item is found, and the popularity of the item).
Regarding claims 7 and 17, Rao discloses all of the limitations as noted above in claims 1 and 14. Rao further discloses wherein communicating the one or more action signals comprises: communicating, to the device associated with the source via the network, a source signal prompting the source to re-arrange a floorplan of a location of the source in relation to the item (Rao: paragraph [0074] - . For another example, an item (or order item) located on a hold rack or in a dressing room for longer than a predetermined time period may initiate a process whereby an employee is sent to relocate the item back to the sales floor of a retail store (thereby making it available to fulfill an order or a different order).).
Regarding claims 8 and 18, Rao discloses all of the limitations as noted above in claims 1 and 14. Rao further discloses wherein: generating the one or more action signals comprises generating, based on the findability score, a message for a picker associated with the online system for prompting the picker to continue searching for the item in a location of the source; and communicating the one or more action signals comprises causing a device of the picker to display a user interface with the message prompting the picker to continue searching for the item in the location of the source (Rao: Figure 6 - instruct a picker to continue looking for the item 608).
Regarding claim 9, Rao discloses all of the limitations as noted above in claim 1. Rao further discloses wherein: generating the one or more action signals comprises selecting, based on the findability score, a set of pickers from a collection of pickers associated with the online system for fulfillment of an order placed at the online system that includes a request for the item; and communicating the one or more action signals comprises causing a set of devices of the set of pickers to display a set of user interfaces with information about the order (Rao: paragraph [0056] - The alternative options 714 may be ranked according to their availability probabilities.).
Regarding claims 10 and 19, Rao discloses all of the limitations as noted above in claims 1 and 14. Rao further discloses wherein: generating the one or more action signals comprises ranking, based at least in part on the findability score, a list of items including the item to generate a ranked list of items, and generating a user interface of a device associated with a user of the online system that includes information about items from the ranked list; and communicating the one or more action signals comprises causing the device associated with the user to display the user interface including a plurality of icons arranged in accordance with the ranking, each of the plurality of icons associated with a respective item from the ranked list (Rao: Figure 6 - instruct a picker to continue looking for the item 608).
Regarding claim 13, Rao discloses all of the limitations as noted above in claim 1. Rao further discloses: collecting feedback data with information about one or more effects of the one or more actions conducted by at least one of the picker, the source, or the user in relation to the item; and re-training the findability prediction machine-learning model by updating, using the collected feedback data, a set of parameters of the findability prediction machine-learning model (Rao: paragraph [0044] - In response to the new information collected by the picker, the modeling engine 218 may update or retrain the machine learning item availability model 216 with the updated training datasets 220. Process 500 may be carried out by the online concierge system 102 until a confidence score associated with a probability that an item is available is above a threshold, paragraph [0026] - The set of functions of the item availability model 216 may be updated and adapted following retraining with new training datasets 220).
Regarding claim 15, Rao discloses all of the limitations as noted above in claim 14. Rao further discloses:
receiving, from the one or more devices of the one or more pickers via the network, the data including one or more picker signals indicating that the item cannot be found in a location of the source (Rao: Figure 6 - instruct a picker to stop looking 610).
receiving, from the device of the source via the network, the data including one or more source signals with information about at least one of inventory of the item in a location of the source or transactions associated with the item in the location of the source over a defined time period (Rao: Figure 4 - predict, using the model, the probability that one of the set of items in the delivery order is available at the warehouse 408).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al (US 2023/0113122 A1) in view of Field-Darragh et al (US 2016/0042315 A1).
Regarding claim 11, Rao discloses all of the limitations as noted above in claim 1. Rao does not expressly disclose wherein: generating the one or more action signals comprises generating, based on the findability score, a message for a user of the online system; and communicating the one or more action signals comprises causing a device associated with the user to display a user interface with the message prompting the user to reschedule an order placed at the online system that includes a request for the item. Field-Darragh discloses:
wherein: generating the one or more action signals comprises generating, based on the findability score, a message for a user of the online system; and communicating the one or more action signals comprises causing a device associated with the user to display a user interface with the message prompting the user to reschedule an order placed at the online system that includes a request for the item (Field-Darragh: paragraph [0215] - If the sku is not found or is found to be in non-sellable condition make that sku unavailable at that location and reschedule the order for routing to another location).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and apparatus of Rao to have included wherein: generating the one or more action signals comprises generating, based on the findability score, a message for a user of the online system; and communicating the one or more action signals comprises causing a device associated with the user to display a user interface with the message prompting the user to reschedule an order placed at the online system that includes a request for the item, as taught by Field-Darragh because it would enable efficient fulfillment of an order (Field-Darragh: paragraph [0015]).
Regarding claim 12, Rao discloses all of the limitations as noted above in claim 1. Rao does not expressly disclose generating training data by assigning labels to a set of items based on a likelihood of finding each item from the set of items at one or more locations of the source when each item from the set is available at the one or more locations of the source; and training, using the training data, the findability prediction machine-learning model to generate a set of initial values for a set of parameters of the findability prediction machine-learning model. Field-Darragh discloses:
generating training data by assigning labels to a set of items based on a likelihood of finding each item from the set of items at one or more locations of the source when each item from the set is available at the one or more locations of the source; and training, using the training data, the findability prediction machine-learning model to generate a set of initial values for a set of parameters of the findability prediction machine-learning model (Field-Darragh: Figure 8 - register tag for item 806).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and apparatus of Rao to have included generating training data by assigning labels to a set of items based on a likelihood of finding each item from the set of items at one or more locations of the source when each item from the set is available at the one or more locations of the source; and training, using the training data, the findability prediction machine-learning model to generate a set of initial values for a set of parameters of the findability prediction machine-learning model, as taught by Field-Darragh because it would enable efficient fulfillment of an order (Field-Darragh: paragraph [0015]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PTO-892 Reference U discloses Design and development of an automated product retrieval system for a warehouse.
US 2024/0242174 A1, Stanke et al discloses SYSTEM AND METHOD FOR DETERMINING AN ON-SHELF AVAILABILITY STATUS OF AN ITEM WITHIN A RETAIL LOCATION.
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KATHLEEN GAGE PALAVECINO
Primary Examiner
Art Unit 3688
/KATHLEEN PALAVECINO/Primary Examiner, Art Unit 3688