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 abstract ideas without significantly more.
Regarding claim 1:
Step 2A Prong 1:
Determining, by the server system, the task to be performed by the entity based, at least in part, on the task-specific query; (Mental process, determining a task from a query can be done in the mind by observation.)
Generating, by the server system via a Large Language Machine learning (LLM) model, a query response message based, at least in part, on the product-specific data, the entity-specific data, and the set of predefined rules, the query response message indicating the task-specific information related to the one or more suitable products for performing the task, the task- specific information comprising a list of the one or more suitable products and the one or more operating parameters corresponding to each suitable product of the one or more suitable products for performing the task; (Mental process, generating a response message can be done in the mind by observing and evaluating information.)
Step 2A Prong 2:
Receiving, by a server system, a task-specific query from an entity, the task-specific query indicating a query requesting task-specific information related to one or more suitable products from a plurality of products to be used for performing a task by the entity; (Adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – Examiner’s note: merely receiving data)
Accessing, by the server system, product-specific data, entity-specific data, and a set of predefined rules from a database associated with the server system based, at least in part, on the entity and the task, the product-specific data comprising information related to each product of the plurality of products, the entity-specific data comprising information related to the entity and the set of predefined rules indicating rules for implementing the plurality of products; (Adding insignificant extra-solution activity to judicial exce3ption MPEP 2106.05(g) – Examiner’s note: merely accessing data)
Transmitting, by the server system, the query response message to the entity in response to the task-specific query. (Adding insignificant extra-solution activity to judicial exception MPEP 2106.05(g) – Examiner’s note: merely sending data)
Step 2B:
The claim does not include additional elements that are sufficient to significantly more than the judicial exceptions.
Receiving, by a server system, a task-specific query from an entity, the task-specific query indicating a query requesting task-specific information related to one or more suitable products from a plurality of products to be used for performing a task by the entity; (Receiving data is well understood routine conventional activity that adds nothing meaningful to the exceptions, see MPEP 2016.05(d)).
Accessing, by the server system, product-specific data, entity-specific data, and a set of predefined rules from a database associated with the server system based, at least in part, on the entity and the task, the product-specific data comprising information related to each product of the plurality of products, the entity-specific data comprising information related to the entity and the set of predefined rules indicating rules for implementing the plurality of products; (Accessing data is well understood routine conventional activity that adds nothing meaningful to the exceptions, see MPEP 2016.05(d)).
Transmitting, by the server system, the query response message to the entity in response to the task-specific query. (Receiving data is well understood routine conventional activity that adds nothing meaningful to the exceptions.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exceptions into a practical application as they are mere insignificant extra solution activity in combinations.
This claim is ineligible.
Regarding claim 2:
The computer-implemented method as claimed in claim 1, wherein receiving the task- specific query, comprises:
facilitating, by the server system, a display of a Graphical User Interface (GUI) on an electronic device associated with the entity, wherein the GUI enables the entity to transmit the task-specific query to the server system as a prompt.
This provides a further description of the method within the abstract ideas, as discussed with regards to claim 1. The limitations of claim 2, under broadest reasonable interpretation, does not include any new abstract ideas.
The claim recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely displays an interface)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions when taken alone or in combination with previous claims. The claimed additional element is just insignificant extra-solution activity. Displaying an interface, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
This claim is ineligible.
Regarding claim 3:
The computer-implemented method as claimed in claim 2, further comprising:
facilitating, by the server system, a display of the query response message on the GUI in response to the prompt of the task-specific query.
This provides a further description of the method within the abstract ideas, as discussed with regards to claim 2. The limitations of claim 3, under broadest reasonable interpretation, does not include any new abstract ideas.
The claim recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely displays a message)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions when taken alone or in combination with previous claims. The claimed additional element is just insignificant extra-solution activity. Displaying message data, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
This claim is ineligible.
Regarding claim 4:
Step 2A Prong 1:
The computer-implemented method as claimed in claim 1, wherein generating the query response message, further comprises:
determining, by the server system via the LLM model, the task-specific information related to the one or more suitable products based, at least in part, on the set of predefined rules; and (Mental process, determining information can be done in the head by observation.)
generating, by the server system, the query response message based, at least in part, on the task-specific information related to the one or more suitable products. (Mental process, generating a message can be done in the head or with pen and paper.)
Step 2A Prong 2:
training, by the server system, the LLM model based, at least in part, on the product- specific data and the entity-specific data, wherein the LLM model is configured to identify the one or more suitable products for performing the task by the entity; (Adding insignificant extra-solution activity to
the judicial exception MPEP 2106.05(g) - Examiner’s note: merely training a model with a dataset is generic and insignificant extra-solution activity.)
Step 2B:
The claim does not include additional elements that are sufficient to significantly more than the judicial exceptions.
training, by the server system, the LLM model based, at least in part, on the product- specific data and the entity-specific data, wherein the LLM model is configured to identify the one or more suitable products for performing the task by the entity; (Training an LLM model is well understood routine and conventional, see MPP 2106.05(d))
The additional elements as disclosed above alone or in combination do not integrate the judicial exceptions into a practical application as they are mere insignificant extra solution activity in combinations.
This claim is ineligible.
Regarding claim 5:
The computer-implemented method as claimed in claim 1, wherein the one or more operating parameters corresponding to each suitable product comprises at least a threshold value for each suitable product for performing the task by the entity.
This provides a further description of the workflow within the abstract ideas, as discussed with regards to claim 1. The limitations of claim 5, under broadest reasonable interpretation, do not include any new abstract ideas.
The claim recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely mentions what the parameter data is)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions when taken alone or in combination with previous claims. The claimed additional element is just insignificant extra-solution activity. Mentioning what the data is, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
The claim is ineligible.
Regarding claim 6:
The computer-implemented method as claimed in claim 1, wherein the information related to each product of the plurality of products comprises product-related presentation information, product-related confluence pages information, and product-related management information.
This provides a further description of the workflow within the abstract ideas, as discussed with regards to claim 1. The limitations of claim 6, under broadest reasonable interpretation, do not include any new abstract ideas.
The claim recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely mentions what the product information is)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions when taken alone or in combination with previous claims. The claimed additional element is just insignificant extra-solution activity. Mentioning what the information compromises, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
The claim is ineligible.
Regarding claim 7:
The computer-implemented method as claimed in claim 1, wherein the LLM model comprises one or more open-source pre-trained large language models.
This provides a further description of the workflow within the abstract ideas, as discussed with regards to claim 1. The limitations of claim 7, under broadest reasonable interpretation, do not include any new abstract ideas.
The claim recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely mentions one or more generic LLMs)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions when taken alone or in combination with previous claims. The claimed additional element is just insignificant extra-solution activity. Mentioning that the LLM is generic, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
This claim is ineligible
Regarding claim 8:
The computer-implemented method as claimed in claim 1, wherein the entity is at least one of an issuer server associated with an issuing bank and an acquirer server associated with an acquiring bank.
This provides a further description of the workflow within the abstract ideas, as discussed with regards to claim 1. The limitations of claim 5, under broadest reasonable interpretation, do not include any new abstract ideas.
The claim recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely mentions the entities are generic servers)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions when taken alone or in combination with previous claims. The claimed additional element is just insignificant extra-solution activity. Mentioning the entities are generic servers, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
This claim is ineligible.
Regarding claim 9:
The computer-implemented method as claimed in claim 1, wherein the server system is a payment server associated with a payment network.
This provides a further description of the workflow within the abstract ideas, as discussed with regards to claim 1. The limitations of claim 5, under broadest reasonable interpretation, do not include any new abstract ideas.
The claim recites additional elements of adding a field of use to the judicial exception MPEP 2106.05(h) – (Examiner’s note: merely mentions the server is used for payments.)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions when taken alone or in combination with previous claims. The claimed additional element is just a field of use. Mentioning the field a server is used, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
This claim is ineligible.
Regarding claims 10 – 17:
Claims 10 – 17 are system claims reciting the same activities as listed by the limitations of claims 1 – 8. The claims are ineligible for having the same judicial exceptions and additional elements. They do not include additional elements that amount to significantly more than the judicial exceptions.
Claims 10 - 17 are ineligible.
Regarding claims 18 - 20:
Claims 18 - 20 are machine claims reciting the same activities as listed by the limitations of claims 1 - 4. The claims are ineligible for having the same judicial exceptions and additional elements. They do not include additional elements that amount to significantly more than the judicial exceptions.
Claims 18 - 20 are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 - 5, 7, 10 - 14, 16, and 18 - 20 are rejected under 35 U.S.C. 103 as being unpatentable by Wu (US 20200242305 A1) over Attali et al (US 20230080674 A1).
Regarding Claim 1:
A computer-implemented method, comprising:
receiving, by a server system, a task-specific query from an entity, the task-specific query indicating a query requesting task-specific information related to one or more suitable products from a plurality of products to be used for performing a task by the entity;
Wu teaches a server system in Fig 1 with chatbot server 130 receiving messages from a chatbot client which were input by a user [0026]. These messages according to [0150 – 0152] are user input queries that are task-specific. The reference teaches that task-specific means having a query intention. [0225] teaches a user’s task-specific query has an intention of product interest relating to suitable products.
determining, by the server system, the task to be performed by the entity based, at least in part, on the task-specific query;
Wu [0152] teaches a query intent classification model that is used to determine what intention or task the user has. [0155] also teaches using this data as input for ranking or determining suitability of products later.
accessing, by the server system, product-specific data, entity-specific data, and a set of predefined rules from a database associated with the server system based, at least in part, on the entity and the task, the product-specific data comprising information related to each product of the plurality of products, the entity-specific data comprising information related to the entity and the set of predefined rules indicating rules for implementing the plurality of products;
Wu [0027] teaches a chatbot database 140 connected to the chatbot server 130. Fig 2 breaks down the data contained in the database. [0042] teaches a product data set 255 that hold information of products or services from a provider. [0044 – 0045] teaches entity specific data in the form of User Profile 256, Energy Intake Record 257, Exercise Record 258, and User Log 259 records relating to the user. [0040 – 0041] teaches predefined rules through a Question-Answer Index 253 and a Knowledge Graph 254, which both contain predefined data sets specific to the task and are called knowledge data [0043]. This knowledge data is used as rules for implementing the products found in the product data set 255. Fig 12 box 1240 and [0221] also mention using a set of task-specific rules based on information and intentions found by the chatbot. An example of the rules when the intention is finding a product of interest is given at [0225]
Generating … a query response message based, at least in part, on the product-specific data, the entity-specific data, and the set of predefined rules, the query response message indicating the task-specific information related to the one or more suitable products for performing the task, the task- specific information comprising a list of the one or more suitable products and the one or more operating parameters corresponding to each suitable product of the one or more suitable products for performing the task; and
Wu [0048] teaches generating a response based on the user’s query using the product data set which holds information on the suitable products. [0234] teaches making a suggestion based on a user profile of entity data, s knowledge graph of predefined rules, and a product data set product data. [0156] teaches the suitable product’s ranking is its associated operating parameter. The highest-ranking product becomes part of the generated response.
transmitting, by the server system, the query response message to the entity in response to the task-specific query.
Wu [0249] teaches providing a message to the user after receiving one or more message from the user.
Wu doesn’t teach “by the server system via a Large Language Machine learning (LLM) model”
Attali does teach “by the server system via a Large Language Machine learning (LLM) model” (Attali [0010] teach using GPT-3 as an LLM for text generation. [0073] teaches that other LLMs can be used to that include pre-trained and open-source models.)
It would have been obvious for one of ordinary skill in that art to combine Attali’s teaching of using an LLM for simple text generation using techniques like few-shot generation. Wu’s embodiment does just that by providing user data and examples of products to be judged, and return a message. Attali’s approach and Wu’s approach are interchangeable but combining the references would lead to determining a more accurate ranking for the suitable products.
Regarding Claim 2:
The computer-implemented method as claimed in claim 1, wherein receiving the task- specific query, comprises:
facilitating, by the server system, a display of a Graphical User Interface (GUI) on an electronic device associated with the entity, wherein the GUI enables the entity to transmit the task-specific query to the server system as a prompt.
Wu [0054] and Fig 3 teach a User Interface that includes virtual buttons, text boxes, and other functions making it a graphical user interface.
Regarding Claim 3:
The computer-implemented method as claimed in claim 2, further comprising:
facilitating, by the server system, a display of the query response message on the GUI in response to the prompt of the task-specific query.
Wu [0059] and Fig 4A teach an example of a display on the GUI with a task specific query response message in response to the input query. They ask about a plurality of shoes and are given information of suitable products in a response.
Regarding Claim 4:
The computer-implemented method as claimed in claim 1, wherein generating the query response message, further comprises:
training, by the server system, the LLM model based, at least in part, on the product- specific data and the entity-specific data, wherein the LLM model is configured to identify the one or more suitable products for performing the task by the entity;
Wu [0186] teaches training the generative model on product and entity data by training the recommendation model portion of it. [0155] also teaches that the input data to this recommendation model includes users profile data and product data.
determining, by the server system via the LLM model, the task-specific information related to the one or more suitable products based, at least in part, on the set of predefined rules; and
Wu [0186] teaches a CTR prediction that recommends potential products with the user inside the recommendation model of the complete generative model. [0155] teaches the CTR prediction is based on the knowledge data which was used as the predefined rules.
generating, by the server system, the query response message based, at least in part, on the task-specific information related to the one or more suitable products.
Wu [0048] teaches generating a response based on the user’s query using the product data set which holds information on the suitable products.
Regarding Claim 5:
The computer-implemented method as claimed in claim 1, wherein the one or more operating parameters corresponding to each suitable product comprises at least a threshold value for each suitable product for performing the task by the entity.
Wu [0156] teaches ranking candidate recommendations where each candidate is a suitable product from the list of products. The operating parameter used is a rank assigned to each candidate and the threshold is being the highest-ranking candidate or number 1 candidate to be recommended to the user.
Regarding Claim 7:
Wu does not teach The computer-implemented method as claimed in claim 1, wherein the LLM model comprises one or more open-source pre-trained large language models.
Attali does teach The computer-implemented method as claimed in claim 1, wherein the LLM model comprises one or more open-source pre-trained large language models. (Attali [0073] teaches using pre-trained open-source models for text generation.)
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20200242305 A1) over Attali et al (US 20230080674 A1) over Evankovich et al (US 20120095828 A1).
Regarding Claim 6:
The combination of Wu and Attali does not teach The computer-implemented method as claimed in claim 1, wherein the information related to each product of the plurality of products comprises product-related presentation information, product-related confluence pages information, and product-related management information.
Evankovich does teach The computer-implemented method as claimed in claim 1, wherein the information related to each product of the plurality of products comprises product-related presentation information, product-related confluence pages information, and product-related management information. (Evankovich [0002] and Fig 4 teaches retailers are creators and publishers, while manufacturers are deployers and advertise products. [0084] teaches product information from both the deployment stage and the creation stage being compared. This makes up product-related confluence page information. [0081] teaches product data related management data where a manger provides parameters to filter the product data. [007] teaches product related presentation data including advertisement presentations.)
It would have been obvious to combine Evankovich’s teaching of information related to each product rather than just Wu’s teaching of product data so that more detailed and relevant data could be used for finding a suitable product for the entity. Evankovich [0005] teaches this data is relevant toward the product’s description. Also, Wu [0156] teaches product data may include requirements for a new product; in which case, having data of prior implementations and products would be beneficial to determining accurate requirements. Combining Evankovich’s, Wu’s, and Attali’s teachings would have helped create more accurate product-related information, and as a result determine suitability of products more accurately.
Claims 8, 9, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20200242305 A1) over Attali et al (US 20230080674 A1) over Pandey et al (US 20210374756 A1).
Regarding Claim 8:
The combination of Wu and Attali does not teach The computer-implemented method as claimed in claim 1, wherein the entity is at least one of an issuer server associated with an issuing bank and an acquirer server associated with an acquiring bank.
Pandey does teach The computer-implemented method as claimed in claim 1, wherein the entity is at least one of an issuer server associated with an issuing bank and an acquirer server associated with an acquiring bank. (Pandey [0043] and Fig 1 teaches an issuer server being called an issuing bank and being associated with the entity.)
It would’ve been obvious to combine Pandey’s teaching of the entity being an issuing server for someone of ordinary skill in the art. Wu’s claimed invention and Pandey’s claimed invention are nearly identical with minor changes and differing environments. Both claim a server system using language models to determine suitability of products, receive a query from the user about the products, and transmit a message to the user. It would have been obvious to use Pandey’s banking environment instead of Wu’s fitness environment, and use Pandey’s entity rather than Wu’s entity, so that there would be increased efficiency in the banking environment when trying to recommend banking products based on suitability.
Regarding Claim 9:
The combination of Wu and Attali does not teach The computer-implemented method as claimed in claim 1, wherein the server system is a payment server associated with a payment network.
Pandey does teach The computer-implemented method as claimed in claim 1, wherein the server system is a payment server associated with a payment network. (Pandey [0040] and Fig 1 teaches a payment server associated with a payment network.)
It would’ve been obvious to combine Pandey’s teaching of a payment server and payment network for someone of ordinary skill in the art. Wu’s claimed invention and Pandey’s claimed invention are nearly identical with minor changes and differing environments. Both claim a server system using language models to determine suitability of products, receive a query from the user about the products, and transmit a message to the user. It would have been obvious to use Pandey’s banking environment instead of Wu’s fitness environment, and use Pandey’s payment server rather than Wu’s server, so that there would be increased efficiency in the banking environment when trying to recommend banking products based on suitability.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AJAY J KANJOOR whose telephone number is (571)270-0965. The examiner can normally be reached Monday-Friday 8am-4pm.
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/AJAY J KANJOOR/ Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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