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
This action is in reply to the claims and response filed on 31 March 2026.
Claims 1, 14 and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 (i.e., an abstract idea) without significantly more.
Step 1: Claim 20 is/are drawn to a system (i.e., a machine), claims 1-13 is/are drawn to a method (i.e., a process), and claims 14-19 is/are drawn to a non-transitory machine-readable storage medium (i.e., a manufacture). As such, claims 1-20 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Representative Claim 1:
receiving sensor data from [a cart] in operation by a picker at a source location;
identifying a triggering event from a plurality of types of triggering events based on the sensor data;
retrieving a template associated with the identified triggering event, wherein the template comprises instructions for generating one or more suggestions for the picker to augment operation of the cart;
obtaining contextual information associated with operation of the cart by the picker;
generating a prompt by modifying the template comprising the instructions to include the sensor data and the contextual information;
transmitting the prompt;
receiving a response output;
generating augmented content including visual content including text describing one or more suggestions for the picker by parsing the response output; and
causing display of the augmented content to [the cart] for presentation during operation of the cart.
As noted by the claim limitations above, the independent claimed invention is directed to providing suggestions to a picker. This is considered to be an abstract idea because it is managing a personal behavior of shopping for an item , which falls within the category of “certain methods of organizing human activity.”
See MPEP 2106.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s): a computing system comprising a processor and a computer-readable medium; one or more sensors of a smart cart; a smart cart; transmitting, to a model serving system, the prompt for execution by a language model; a model serving system; a response output by the language model; and an electronic display of the smart cart. This/these additional elements individually or in combination do not integrate the exception into a practical application because they merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Further, the physical smart cart is also merely generally linking the abstract idea to a particular field of use, whether viewed individually or as an ordered combination (see MPEP 2106.05(h)). Accordingly, these additional element(s) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) merely use a computer as a tool to perform an abstract idea and generally linking the abstract idea to a particular field of use, which does not render a claim as being significantly more than the judicial exception. Accordingly, claim 1 is ineligible.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claim 1 is not eligible subject matter under 35 USC 101.
Dependent claim(s) 2, 4-5, 7, and 10-11 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 1. Therefor claim(s) 2, 4-5, 7, and 10-11 are ineligible.
Dependent claim(s) 3, 6, 8-9, and 12-13 further recite(s) the additional element(s): one or more populatable fields (claim 3), a speech audio byte (claim 6), training the language model (claim 8), retraining the language model (claim 9), an acoustic sensor (claim 12), a camera (claim 13), and virtual content (claim 13). This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 3, 6, 8-9, and 12-13 is/are ineligible.
Claim 14 is parallel in nature to claim 1. Claim 14 recites an abstract idea similar in nature to claim 1. Furthermore, claim 14 recites the following additional elements: a non-transitory computer-readable medium storing instructions that when executed by a processor, cause the processor to perform operations; one or more sensors of a smart cart; a smart cart; transmitting, to a model serving system, the prompt for execution by a language model; a model serving system; a response output by the language model; and an electronic display of the smart cart. These additional elements do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea.
Dependent claim(s) 15, 17, 19 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 14. Therefor claim(s) 15, 17, and 19 are ineligible.
Dependent claim(s) 16, 18 further recite(s) the additional element(s): one or more populatable fields (claim 16) and a speech audio byte (claim 18). This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 16 and 18 is/are ineligible.
Claim 20 is parallel in nature to claim 1. Claim 20 recites an abstract idea similar in nature to claim 1. Furthermore, claim 20 recites the following additional elements: a processor; a non-transitory computer-readable medium storing instructions that when executed by the processor, cause the processor to perform operations; one or more sensors of a smart cart; a smart cart; transmitting, to a model serving system, the prompt for execution by a language model; a model serving system; a response output by the language model; and an electronic display of the smart cart. These additional elements do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 12-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCarthy (US 20150242918 A1) in view of Wheeler (US 20240356881 A1).
Regarding claim 1, McCarthy teaches a method, performed at a computer system comprising a processor and a computer-readable medium, (Paragraph [0078] “a processor 702 coupled to internal memories 704 and 706”) comprising:
receiving sensor data from one or more sensors of a smart cart in operation by a picker at a source location; (see at least Paragraph [0041] “A personal shopper system may be comprised of one or more device, such as a headset with a speaker and microphone, a visual display (e.g., a tablet, retail scanner, goggle display, etc.), a customized cart, etc., that may enable a retail store server to provide indications to a personal shopper to direct the personal shopper in picking items for an order (or cart).”; Paragraph [0045] “the retail store server may sort items or an order (or cart) to be picked based at least in part on the current location of the personal shopper system of the personal shopper assigned to pick the items”)
identifying a triggering event from a plurality of types of triggering events based on the sensor data; (Paragraph [0073] “In block 508 the personal shopper system may receive a voice input of a next item and quantity from the customer and send the voice input to the retail store server.”; Fig. 5)
(Paragraph [0074] “In block 516 the retail store server may send a voice output of the next item location in the store in the selected language for the customer and in block 518 the personal shopper system may receive and output the voice output of the next item location in the retail store.”; Fig. 5)
obtaining contextual information associated with operation of the smart cart by the picker; (Paragraph [0073] “In block 508 the personal shopper system may receive a voice input of a next item and quantity from the customer and send the voice input to the retail store server.”; Fig. 5)
receiving, from a model serving system, a response output by the language model; (Paragraph [0074] “In block 516 the retail store server may send a voice output of the next item location in the store in the selected language for the customer and in block 518 the personal shopper system may receive and output the voice output of the next item location in the retail store.”; Paragraph [0055] “The various embodiment voice ordering systems and/or voice directed picking systems may apply various language processing techniques to identify voice inputs received from customers and/or personal shoppers, including stored word libraries, natural language processing, etc.”; Fig. 5 Examiner notes the voice input from paragraph [0073] is analyzed by a language model and the model outputs a response as described in Paragraph [0074])
causing display of the augmented content on an electronic display of the smart cart during operation of the smart cart. (Paragraph [0074] “In block 516 the retail store server may send a voice output of the next item location in the store in the selected language for the customer and in block 518 the personal shopper system may receive and output the voice output of the next item location in the retail store.”; Paragraph [0041] “A personal shopper system may be comprised of one or more device, such as […] a visual display (e.g., a tablet, retail scanner, goggle display, etc.)”; Paragraph [0046] “In an embodiment, the next item may be the item name and size and may include other descriptions, such as color, label features, or an image of the item sent to a display of the personal shopper system”; Fig. 5)
McCarthy does not teach:
retrieving a template associated with the identified triggering event, wherein the template comprises instructions for generating one or more suggestions for the picker to augment operation of the smart cart;
generating a prompt by modifying the template comprising the instructions to include the sensor data and the contextual information;
transmitting, to a model serving system, the prompt for execution by a language model; and
generating augmented content including visual content including text describing one or more suggestions for the picker by parsing the response output by the language model.
However Wheeler teaches:
retrieving a template associated with the identified triggering event, wherein the template comprises instructions for generating one or more suggestions; (Paragraph [0182] “In block 1204, the method 1200 may continue with generating a text input prompt for the LLM based on a scenario template. […] The scenario template may further include rules to instruct, in block 1208, the LLM to analyze text input prompts and compute response outputs of the LLM as defined by a role of the LLM in the scenario.”; step 1203 and 1208 of Fig. 12 of Wheeler)
generating a prompt by modifying the template comprising the instructions to include the sensor data and the contextual information; (Paragraph [0181] “The method 1200 may commence in block 1202 with receiving input messages […] The plurality of sources may include one or more of the following: data events, machines, sensors, further bots, and further sources such as databases or systems accessible to a communication party of the plurality of communication parties”; Paragraph [0182] “In block 1204, the method 1200 may continue with generating a text input prompt for the LLM based on a scenario template. The scenario template may include rules to organize, in block 1206, the input messages based on the scenario.”; step 1202 and 1204 of Fig. 12 of Wheeler)
transmitting, to a model serving system, the prompt for execution by a language model; (Paragraph [0187] “In block 1210, the method 1200 may proceed with sending the text input prompt to the LLM.”; step 1210 of Fig. 12 of Wheeler) and
generating augmented content including visual content including text describing one or more suggestions for the picker by parsing the response output by the language model. (Paragraph [0188] “In block 1212, the method 1200 may include analyzing the response output of the LLM. […] Based on the analysis of the response output of the LLM, output messages to be sent to one or more communication parties of the plurality of communication parties may be generated.”; Paragraph [0198] “the LLM may be configured to recognize […] and suggests”; Paragraph [0074] “the response output may be in a combination of modalities, such as text, an image, audio, video”; step 1212 of Fig. 12 of Wheeler)
This step of Wheeler is applicable to the method of McCarthy as they both share characteristics and capabilities, namely, they are directed to using language models to parse text and communicate results to another entity. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of McCarthy to incorporate retrieving a template associated with the identified triggering event, wherein the template comprises instructions for generating one or more suggestions, generating a prompt by modifying the template comprising the instructions to include the sensor data and the contextual information; transmitting the prompt for execution by a language model; and generating augmented content including one or more suggestions for the picker by parsing the response output by the language model as taught by Wheeler. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify McCarthy in order to provides and facilitates interactions between the communication parties (see Paragraph [0047] of Wheeler).
Regarding claim 2, McCarthy in view of Wheeler teaches the method of claim 1. McCarthy further teaches:
wherein identifying the triggering event comprises:
identifying, based on the sensor data, whether each of one or more triggering criteria is satisfied; (Paragraph [0074] “In determination block 520 the personal shopper system may determine whether a finishing shopping indication is received. For example a finish shopping indication may be a button press event or spoken command. In response to not receiving a finish shopping indication (i.e., determination block 520="No"), in block 508 the personal shopper system may receive[d] and send a voice input of the next item and quantity. In response to receiving a finish shopping indication (i.e., determination block 520="Yes"), in blocks 522 and 524 the personal shopper system and retail store server may close the connection with one another.”; Fig. 5) and
based on identifying that each of the one or more triggering criteria is satisfied, identifying that the triggering event occurred. (Paragraph [0074] “In determination block 520 the personal shopper system may determine whether a finishing shopping indication is received. For example a finish shopping indication may be a button press event or spoken command. In response to not receiving a finish shopping indication (i.e., determination block 520="No"), in block 508 the personal shopper system may receive[d] and send a voice input of the next item and quantity. In response to receiving a finish shopping indication (i.e., determination block 520="Yes"), in blocks 522 and 524 the personal shopper system and retail store server may close the connection with one another.”; Fig. 5)
Regarding claim 3, McCarthy in view of Wheeler teaches the method of claim 1. McCarthy does not teach:
wherein the template comprises one or more populatable fields, and wherein generating the prompt comprises inputting information from the sensor data or the contextual information into each populatable field.
However, Wheeler teaches:
wherein the template comprises one or more populatable fields, and wherein generating the prompt comprises inputting information from the sensor data or the contextual information into each populatable field (Paragraph [0099] “the instructions about the syntax structure of incoming messages may include “Messages from the customer begin with ‘customer:,’ messages from the driver begin with ‘driver:,’ and messages from the employee begin with ‘employee:.’”; Paragraph [0183] “the scenario template may further include a first syntax structure and a second syntax structure.”; Paragraph [0181] “the input messages may originate from a plurality of sources. […] The plurality of sources may include one or more of the following: data events, machines, sensors, further bots, and further sources such as databases or systems accessible to a communication party of the plurality of communication parties and enterprises associated with the plurality of communication parties.” of Wheeler)
The motivation for making this modification to the teachings of McCarthy is the same as that set forth above, in the rejection of claim 1.
Regarding claim 4, McCarthy in view of Wheeler teaches the method of claim 1. McCarthy does not teach:
wherein generating the prompt comprises generating the prompt to include multimodal data.
However, Wheeler teaches:
wherein generating the prompt comprises generating the prompt to include multimodal data. (Paragraph [0074] “the input prompt may be in a combination of modalities, such as text, an image, audio, video, a location, and any other data type or data format.” of Wheeler)
The motivation for making this modification to the teachings of McCarthy is the same as that set forth above, in the rejection of claim 1.
Regarding claim 5, McCarthy in view of Wheeler teaches the method of claim 1. McCarthy further teaches:
wherein the language model is configured to output the response to include multimodal data, and wherein generating the augmented content comprises generating the augmented content to include multimodal data. (Paragraph [0074] “In block 516 the retail store server may send a voice output of the next item location in the store in the selected language for the customer and in block 518 the personal shopper system may receive and output the voice output of the next item location in the retail store”; Paragraph [0046] “In an embodiment, the next item may be the item name and size and may include […] an image of the item sent to a display of the personal shopper system.”)
Regarding claim 6, McCarthy in view of Wheeler teaches the method of claim 1. McCarthy further teaches:
wherein generating the augmented content comprises:
generating a speech audio byte for each suggestion. (Paragraph [0074] “In block 516 the retail store server may send a voice output of the next item location in the store in the selected language for the customer and in block 518 the personal shopper system may receive and output the voice output of the next item location in the retail store”)
McCarthy does not teach:
parsing the response to identify one or more suggestions for the picker.
However, Wheeler teaches:
parsing the response to identify one or more suggestions for the picker. (Paragraph [0188] “In block 1212, the method 1200 may include analyzing the response output of the LLM. […] Based on the analysis of the response output of the LLM, output messages to be sent to one or more communication parties of the plurality of communication parties may be generated.”; Paragraph [0198] “the LLM may be configured to recognize […] and suggests”; step 1212 of Fig. 12 of Wheeler)
The motivation for making this modification to the teachings of McCarthy is the same as that set forth above, in the rejection of claim 1.
Regarding claim 12, McCarthy in view of Wheeler teaches the method of claim 1. McCarthy further teaches:
wherein receiving the sensor data comprises receiving audio data from an acoustic sensor, wherein the audio data captures speech by the picker; (Paragraph [0073] “In block 508 the personal shopper system may receive a voice input of a next item and quantity from the customer”; Paragraph [0078] “The computing device 700 may also include […] microphones 715.”)
wherein identifying the triggering event comprises identifying an inquiry by the picker in the audio data; (Paragraph [0073] “In block 508 the personal shopper system may receive a voice input of a next item and quantity from the customer”)
wherein generating the prompt comprises generating the prompt to include the inquiry by the picker and instructions to generate a response to the inquiry; (Paragraph [0055] “The various embodiment voice ordering systems and/or voice directed picking systems may apply various language processing techniques to identify voice inputs received from customers and/or personal shoppers, including stored word libraries, natural language processing, etc.”) and
wherein generating the augmented content comprises generating one or more responses to the inquiry of the picker. (Paragraph [0074] “In block 516 the retail store server may send a voice output of the next item location in the store in the selected language for the customer and in block 518 the personal shopper system may receive and output the voice output of the next item location in the retail store.”)
Regarding claim 13, McCarthy in view of Wheeler teaches the method of claim 12. McCarthy further teaches:
wherein receiving the sensor data further comprises receiving image data from a camera; (Paragraph [0057] “the customized cart may identify items as the items are placed in the cart, for example using […] a camera.”)
wherein generating the augmented content comprises generating virtual content to append to the image data. (Paragraph [0046] “the retail store server may provide indications of a next item to pick to a personal shopper system and the personal shopper system may output the indications to the personal shopper, […] the next item may be […] an image of the item sent to a display of the personal shopper system.”
McCarthy does not teach:
wherein generating the prompt further comprises generating the prompt to include the image data.
However, Wheeler teaches:
wherein generating the prompt further comprises generating the prompt to include the image data. (Paragraph [0074] “the input prompt may be in a combination of modalities, such as […] an image”)
The motivation for making this modification to the teachings of McCarthy is the same as that set forth above, in the rejection of claim 1.
Claims 14-18:
Claim(s) 14-18 is/are directed to a non-transitory computer-readable storage medium. Claim(s) 14-18 recite limitations parallel in nature as those addressed above for claim(s) 1-4 and 6 which are directed towards a method. Claim(s) 14-18 is/are therefore rejected for the same reasons as set above for claim(s) 1-4 and 6, respectively. Claims 14-18 further recite: a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform operations (Paragraph [0121] of McCarthy).
Claim 20:
Claim(s) 20 is/are directed to a system. Claim(s) 20 recite limitations parallel in nature as those addressed above for claim(s) 1, which are directed towards a method. Claim(s) 20 is/are therefore rejected for the same reasons as set above for claim(s) 1, respectively. Claim 20 further recites a processor and a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform operations (Paragraph [0121] of McCarthy).
Claim(s) 7 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCarthy (US 20150242918 A1) in view of Wheeler (US 20240356881 A1) in further view of Chaubard (US 20230306451 A1).
Regarding claim 7, McCarthy in view of Wheeler teaches the method of claim 1.
McCarthy further teaches:
a language model. (Paragraph [0055] “natural language processing”)
McCarthy in view of Wheeler does not teach:
identifying one or more actions performable by an autonomous agent from the response output by the language model, wherein one action performable by the autonomous agent includes modifying an order being serviced by the picker; and
modifying, via the autonomous agent, the order being serviced by the picker.
However, Chaubard teaches:
identifying one or more actions performable by an autonomous agent from the response output by a machine learning model (Paragraph [0047] “when predetermined conditions are met, and based on the output from the machine learning model, the recommendation module 243 may automatically perform actions like […] instruct a store associate to take necessary steps to implement the updated planogram on the store shelves, […] change the reorder rate or quantity for a particular product and transmit an updated order to a third party vendor for the updated reorder rate or quantity” of Chaubard) and
modifying, via the autonomous agent, the order being serviced by the picker. (Paragraph [0047] “the recommendation module 243 may automatically perform actions like […] instruct a store associate to take necessary steps to implement the updated planogram on the store shelves, […] change the reorder rate or quantity for a particular product and transmit an updated order to a third party vendor for the updated reorder rate or quantity” of Chaubard)
This step of Chaubard is applicable to the method of McCarthy as they both share characteristics and capabilities, namely, they are directed to making recommendations to a store order based on machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of McCarthy to incorporate identifying actions performable by an autonomous agent and modifying the order being serviced by the picker as taught by Chaubard. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify McCarthy in order to determine item substitutions and drive a recommendation engine based on substitution determinations and parameters relating to substitutions (see paragraph [0002] of Chaubard).
Claim 19:
Claim(s) 19 is/are directed to a non-transitory computer-readable storage medium. Claim(s) 19 recites limitations parallel in nature as those addressed above for claim(s) 7, which are directed towards a method. Claim(s) 19 is/are therefore rejected for the same reasons as set above for claim(s) 7, respectively. Claims 19 further recites: a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform operations (Paragraph [0121] of McCarthy).
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCarthy (US 20150242918 A1) in view of Wheeler (US 20240356881 A1) in further view of Schooler (US 12056741 B1).
Regarding claim 8, McCarthy in view of Wheeler teaches the method of claim 1. McCarthy further teaches:
a language model. (Paragraph [0055] “natural language processing”)
McCarthy in view of Wheeler does not teach:
wherein the language model is trained by:
obtaining preference data describing one or more preferences by the picker; and
training the language model with the preference data to bias generating suggestions to account for the one or more preferences by the picker.
However, Schooler teaches:
wherein the machine learning model is trained by:
obtaining preference data describing one or more preferences by the picker; (Col. 22, ll. 41-51 “feature engineering 1004 is used to identify features 906”; Col. 22, ll. 52- Col. 23, ll. 17 “Features 906 may also be […] user data 920, […] User data can include […] user preferences” of Schooler) and
training the machine learning model with the preference data to bias generating suggestions to account for the one or more preferences by the picker. (Col. 14, ll. 4-10 “The machine learning algorithm can be trained on the preferences of both digital content item providers” of Schooler)
These steps of Schooler is applicable to the method of McCarthy as they both share characteristics and capabilities, namely, they are directed to using a machine learning model. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of McCarthy to incorporate training the machine learning model as taught by Schooler. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify McCarthy in order to train the machine learning model to provide recommendations (see Col. 14, ll. 24-33 of Schooler).
Regarding claim 9, McCarthy in view of Wheeler teaches the method of claim 8. McCarthy further teaches:
a language model. (Paragraph [0055] “natural language processing”)
McCarthy in view of Wheeler does not teach:
receiving feedback from the picker in response to the augmented content either adopting or rejecting at least one suggestion presented in the augmented content;
generating a reinforcement training example based on the feedback from the picker; and
retraining the language model with at least the reinforcement training example.
However, Schooler teaches:
receiving feedback from the picker in response to the augmented content either adopting or rejecting at least one suggestion presented in the augmented content; (Col. 22, ll. 30-33 “Validation, refinement or retraining 1012: This may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback.”; step 1012 of Fig. 10 of Schooler)
generating a reinforcement training example based on the feedback from the picker; (Col. 45-65 “Examples of machine learning algorithms can be […] reinforcement learning. […] Reinforcement learning involves training a model to make decisions in a dynamic environment by receiving feedback in the form of rewards or penalties. […] Examples of reinforcement learning algorithms include Q-learning and policy gradient methods.”; Col. 22, ll. 30-33 “Validation, refinement or retraining 1012: This may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback.”; step 1012 of Fig. 10 of Schooler) and
retraining the machine learning model with at least the reinforcement training example. (Col. 22, ll. 30-33 “Validation, refinement or retraining 1012: This may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback.”; step 1012 of Fig. 10 of Schooler)
The motivation for making this modification to the teachings of McCarthy is the same as that set forth above, in the rejection of claim 8.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCarthy (US 20150242918 A1) in view of Wheeler (US 20240356881 A1) in further view of Drozd (US 11372876 B1) in further view of Seiflein (US 20160037137 A1).
Regarding claim 10, McCarthy in view of Wheeler teaches the method of claim 1. McCarthy further teaches:
obtaining historical data describing one or more past orders serviced by the picker; (Paragraph [0041] “the retail store server may track personal shopper performance”)
McCarthy in view of Wheeler does not teach:
wherein identifying the triggering event comprises identifying that the picker has visited the source location less than a threshold number of instances based on the historical data;
wherein generating the prompt comprises generating the prompt to include instructions to contextualize the source location to other source locations from the historical data of the picker; and
wherein generating the augmented content comprises generating one or more suggestions describing a layout of the source location as compared to layouts of the other source locations.
However, Drozd teaches:
wherein identifying the triggering event comprises identifying that the picker has visited the source location less than a threshold number of instances based on the historical data; (Col. 1, ll. 39-59 “the local business recommendation system may determine that the user is unfamiliar with the geographic area when […] when the user has visited the geographic area or passed by the geographic area less than a threshold number of times.” of Drozd)
This step of Drozd is applicable to the method of McCarthy as they both share characteristics and capabilities, namely, they are directed to providing a recommendation (i.e. a suggestion) to a user. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of McCarthy to incorporate identifying that the picker has visited the source location less than a threshold number of times as taught by Drozd. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify McCarthy in order to provide recommendations and search results to a user visiting a geographic area (see Col. 1, ll. 8-12 of Drozd).
McCarthy in view of Wheeler in further view of Drozd does not teach:
wherein generating the prompt comprises generating the prompt to include instructions to contextualize the source location to other source locations from the historical data of the picker; (Paragraph [0074] “The system and methods disclosed herein can also provide access to software or hardware that can assist in separate, and the combined use of […] artificial intelligence”; Paragraph [0063] “Using the methods and systems described herein, system can create a layout of the present location, compare the information to known locations” of Seiflein) and
wherein generating the augmented content comprises generating one or more suggestions describing a layout of the source location as compared to layouts of the other source locations. (Paragraph [0063] “Using the methods and systems described herein, system can create a layout of the present location, compare the information to known locations” of Seiflein)
This step of Seiflein is applicable to the method of McCarthy as they both share characteristics and capabilities, namely, they are directed to a device to guide a user to a destination. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of McCarthy to incorporate using AI to generate suggestions describing a layout of a location compared to other locations as taught by Seiflein. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify McCarthy in order to provide directional help to a user (see paragraph [0063] of Seiflein).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCarthy (US 20150242918 A1) in view of Wheeler (US 20240356881 A1) in further view of Subramanian (US 20180338031 A1).
Regarding claim 11, McCarthy in view of Wheeler teaches the method of claim 1. McCarthy further teaches:
wherein receiving the sensor data comprises receiving location data from a tracking system, wherein the location data tracks location of the smart cart in the source location; (Paragraph [0043] “a personal shopper system may include a GPS receiver and determine its current GPS coordinates (e.g., latitude, longitude, elevation, etc.)”)
McCarthy in view of Wheeler does not teach:
wherein identifying the triggering event comprises identifying that the picker is entering or leaving one department of the source location based on the location data;
wherein generating the prompt comprises generating the prompt to include instructions to provide reminders for one or more items to be obtained in the department; and
wherein generating the augmented content comprises generating one or more reminders to obtain the one or more items before leaving the department.
However, Subramanian teaches:
wherein identifying the triggering event comprises identifying that the picker is entering or leaving one department of the source location based on the location data; (Paragraph [0063] “At OPERATION 312, the plurality of geofences 122 associated with the reminder 106 are monitored, for example, for determining whether the user's mobile computing device 104 triggers a predetermined percentage of the geofences 122 associated with the reminder 106 at DECISION OPERATION 314. When a determination is made that the user's mobile computing device 104 has not triggered a predetermined percentage of the geofences 122 associated with the reminder 106, the method 300 returns to OPERATION 312, where the plurality of geofences continue to be monitored.”; el. 312 of Fig 3 of Subramanian)
wherein generating the prompt comprises generating the prompt to include instructions to provide reminders for one or more items to be obtained in the department; (Paragraph [0059] “The method 300 starts at OPERATION 302, and proceeds to OPERATION 304, where an indication of a selection to set an intent-based reminder 106 is received.”; Paragraph [0060] “the reminder 106 may be a reminder to grab an item before leaving a location 202”; step 304 and 306 of Fig. 3 of Subramanian) and
wherein generating the augmented content comprises generating one or more reminders to obtain the one or more items before leaving the department. (Paragraph [0060] “the reminder 106 may be a reminder to grab an item before leaving a location 202”; Paragraph [0064] “OPERATION 316, where the reminder 106 is provided to the user 102.”; step 316 of Fig. 3 of Subramanian)
These steps of Subramanian is applicable to the method of McCarthy as they both share characteristics and capabilities, namely, they are directed to helping a user gather items in a source area with machine intelligence. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of McCarthy to incorporate using machine intelligence to provide reminders to obtain an item before leaving an area as taught by Subramanian. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify McCarthy in order to save the user from forgetting an item (see paragraph [0005] of Subramanian).
Response to Arguments
Applicant’s arguments, see Page 10, filed 31 March 2026, with respect to claim objections have been fully considered and are persuasive due to the amendments of the independent claims. The claim objections of claims 1-20 have been withdrawn.
Applicant's arguments, see Page(s) 11-14, filed 31 March 2026, with respect to the 35 USC § 101 rejection(s) of claim(s) 1-20 have been fully considered but they are not persuasive. Applicant argues 1) the claims are not directed to an abstract idea; 2) the claims are integrated into a practical application; and 3) the claims recite significantly more than the abstract idea. The Examiner respectfully disagrees.
Regarding argument 1, the Applicant argues the claims are not directed to an abstract idea. The Examiner respectfully disagrees. MPEP 2106.04(a)(2)II. recites:
Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping.
The claims recite a human (i.e. a picker) interacting with a smart cart, which is a single person interacting with a computer. This falls under “certain method of organizing human activity” under the subcategory of managing personal behavior (see MPEP 2106.04(a)(2)(II)). USPTO guidance uses the term ‘‘additional elements’’ to refer to claim features, limitations, and/or steps that are recited in the claim beyond the identified judicial exception. The applicant argues “generating a prompt by modifying the template comprising the instructions to include the sensor data and the contextual information;” “transmitting, to a model serving system, the prompt for execution by a language model;” “receiving, from the model serving system, a response output by the language model;” “generating augmented content including visual content including text describing one or more suggestions for the picker by parsing the response output by the language model;” and “causing display of the augmented content on an electronic display of the smart cart during operation of the smart cart” are all addition elements. As explained in the above 101 rejection, “generating a prompt by modifying the template comprising the instructions to include the sensor data and the contextual information;” “generating augmented content including visual content including text describing one or more suggestions for the picker by parsing the response output” and “causing display of the augmented content during operation of the cart” are part of the abstract idea because they discuss actions at a high level. A human would perform the steps in claimed invention the same way a generic computer would. “Transmitting, to a model serving system, the prompt for execution by a language model;” “receiving, from the model serving system, a response output by the language model;” “the language model”; and “an electronic display of the smart cart” are all additional elements and will be discussed in the response to arguments 2 and 3. The Examiner maintains that an abstract idea is recited.
Regarding argument 2, the Applicant argues the claims are integrated into a practical application. The Examiner respectfully disagrees. MPEP 2106.05(f) recites:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The claimed additional elements of “transmitting, to a model serving system, the prompt for execution by a language model;” “receiving, from the model serving system, a response output by the language model;” “the language model”; and “an electronic display of the smart cart” serve as an aid to a user that is shopping to improve the shopping experience. These additional elements provide improvements that are inherent with applying the abstract idea (i.e. shopping) on a generic computing device. The additional elements are performing tasks the same way a human would and the efficiency come from applying the tasks to a generic computing environment. Since the improvement does not go beyond the identified judicial exception, the improvement is not considered to be technical in nature. Therefore the invention is an improvement to the abstract idea and not to a specific technical problem.
Regarding argument 3, the Applicant argues the additional elements provide significantly more than the abstract idea because they are not well understood, routine, or conventional. The Examiner is not arguing the additional elements are well understood, routine, or conventional, but generic computing elements (see MPEP 2106.05(f)). As explained in argument 2, the additional elements are providing high level steps and are performing tasks the same way a human would. The efficiency come from applying the tasks to a generic computing environment. Therefore, the additional elements to do not provide significantly more than the abstract idea. The Examiner maintains that claims 1-20 are not eligible under the Alice/Mayo test for eligibility.
Applicant's arguments, see Page(s) 15-17, filed 31 March 2026, with respect to the 35 USC § 103 rejection(s) of claim(s) 1-20 have been fully considered but they are not persuasive. Applicant argues the cited prior art does not teach the claims as amended. The Examiner respectfully disagrees.
The Applicant argues McCarthy in view of Wheeler does not teach “evaluating the sensor data to identify moments that would benefit from AI-assistance to the picker” and “deployment of a language model to generate tailored suggestions to the picker, which is used to create augmented content”. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The Examiner is relying on McCarthy to teach “evaluating the sensor data to identify moments that would benefit from AI-assistance to the picker”. Paragraph [0041] of McCarthy recites:
Whether orders are taken by a central server and/or a retail store server, in various embodiments, a retail store server may select a personal shopper to pick the items for the order (or cart) and provide commands (e.g., voice commands, visual commands, etc.) to the personal shopper to guide the personal shopper to pick the items of the order. In an embodiment, a personal shopper may be a retail store employee using a personal shopper system. A personal shopper system may be comprised of one or more device, such as a headset with a speaker and microphone, a visual display (e.g., a tablet, retail scanner, goggle display, etc.), a customized cart, etc., that may enable a retail store server to provide indications to a personal shopper to direct the personal shopper in picking items for an order (or cart). In an embodiment, the retail store server may select a personal shopper based on availability and/or performance metrics, such as a time worked, average picking speed, distance traveled, qualification level, etc. to pick an order. In an embodiment, the retail store server may reassign personal shoppers from one task, such as working a cash register, to another task, such as picking items for an order, based on the number of customers currently in the store or other factors impacting staffing decisions. In this manner, the retail store server may utilize personal shopper systems worn by store employees to manage operations of a retail store, such as a grocery store. Additionally, the retail store server may track personal shopper performance and send incentives to the personal shopper to improve personal shopper output, such as pick speed. The various embodiments may also enable any individual to operate as a personal shopper because the personal shopper system may provide directions to that individual to guide his or her picking in the individual's native language.
Paragraph [0043] of McCarthy recite multiple sensors used to get information about the environment of the shopper system including audio sensors (i.e. microphones). Paragraph [0073] of McCarthy recites:
FIG. 5 is a process flow diagram illustrating an embodiment method for voice assisted shopping. In an embodiment, a personal shopper system may be given to a customer when the customer enters a retail store to assist the customer in finding items in the retail store. In block 502 the personal shopper system may receive a start shopping indication. For example, the start shopping indication may be an indication of a button press event or a spoken command by the customer. In blocks 504 and 506 the personal shopper system and retail store server may establish connections with one another. In block 508 the personal shopper system may receive a voice input of a next item and quantity from the customer and send the voice input to the retail store server. In block 510 the retail store server may receive the voice input of the next item and quantity.
Paragraph [0073] and Fig. 5 of McCarthy shows that the audio information is used as a trigger to send the next item location to the shopper. Therefore, McCarthy teaches “evaluating the sensor data to identify moments that would benefit from AI-assistance to the picker”. McCarthy is silent to “deployment of a language model to generate tailored suggestions to the picker, which is used to create augmented content”. However, paragraph [0181] of Wheeler recites:
The method 1200 may commence in block 1202 with receiving input messages from user devices associated with a plurality of communication parties participating in an instance of a scenario. The LLM may be configured to understand the scenario. In an example embodiment, the input messages may originate from a plurality of sources. The plurality of sources is shown as external data source 708 in FIG. 7. The plurality of sources may include one or more of the following: data events, machines, sensors, further bots, and further sources such as databases or systems accessible to a communication party of the plurality of communication parties and enterprises associated with the plurality of communication parties.
Wheeler described an LLM that is able to take in sensor inputs to generate an output. The shopping cart computer system in McCarthy can be modified by the LLM in Wheeler by one of the ordinary skill in the art to create the claimed invention because they are both directed to using computer systems to analyze language and generate an output for a user. Furthermore, paragraph [0074] of Wheeler recites:
In a further embodiment, the input prompt may be in a combination of modalities, such as text, an image, audio, video, a location, and any other data type or data format. A modality may refer to a category of data defined by how the data are received, represented, and understood. In a further embodiment the response output may be in a combination of modalities, such as text, an image, audio, video, a location, and any other data type or data format.
Paragraph [0074] of Wheeler explains that the output from the LLM can be a combination of text and image/video, which would be visual content including text. Therefore, the Examiner maintains that the combination of McCarthy in view of Wheeler teaches the amended claims 1-20.
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 DANIELLE ELIZABETH ZEVITZ whose telephone number is (703)756-1070. The examiner can normally be reached Mo-Th 10am-6pm.
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/DANIELLE ELIZABETH ZEVITZ/Examiner, Art Unit 3628 /GEORGE CHEN/Primary Examiner, Art Unit 3628
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