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 .
Claim Objections
Claims 1-7 are objected to because of the following informalities: At the end of the fourth line of claim 1, “object” should be followed by a semicolon rather than a comma. In the sixth line of claim 1, “memory” should be followed by a semicolon rather than a comma. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: In the second and third lines of claim 6, “such that the summary viewable” should be “such that the summary is viewable”. Appropriate correction is required.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention lacks patentable utility.
First, it is determined that the claims are directed to a statutory category of invention. See MPEP 2106.03 (II). In the instant case, claims 1-7 are directed to a system comprising one or more servers, and therefore fall within the statutory category of machine. Claim 8 is directed to an information processing method, and therefore falls with the statutory category of process. Claim 9 is directed to a non-transitory computer readable medium for storing a program for causing one or multiple computers to perform operations, and therefore falls with the statutory category of article of manufacture. Therefore, claims 1-9 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. See MPEP 2106.04. The claims are analyzed to evaluate whether they recite a judicial exception (Step 2A, Prong One) as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Step 2A, Prong Two). See MPEP 2106.04. Representative claim 1 recites (technological elements bolded):
An information processing system, comprising one or multiple servers, wherein
at least one of the one or multiple servers is configured to obtain a description of a transaction object input by an administrator of the transaction object;
at least one of the one or multiple servers is configured to obtain a guideline related to a summary of the description from a memory; and
at least one of the one or multiple servers is configured to transmit the description and the guideline to a summarization model to obtain a summary of the description in accordance with the guideline.
Representative claim 8 is even less technological, in that it merely recites steps which the recited method “caus[es] one or multiple computers to” perform:
An information processing method, the method causing one or multiple computers to perform:
obtain a description of a transaction object input by an administrator of the transaction object;
obtain a guideline related to a summary of the description; and
send the description and the guideline to a summarization model to obtain a summary of the description in accordance with the guideline.
Under Step 2A, Prong One, the claims are directed to obtaining a summary of a description of a transaction object, such as an object offered for sale, and therefore to commercial or legal interactions (including: advertising, marketing or sales activities of behaviors; business relations). The summary is applicable to advertising or marketing the transaction object, or determining whether the transaction object is of interest to a potential buyer. Abstract ideas constitute a judicial exception, and are therefore not patent-eligible. Commercial interactions are a form of abstract idea, in the category of certain methods of organizing human activity.
Further under Step 2A, Prong One, the claims are directed to mental processes. Mental processes are another of abstract idea, and therefore a judicial exception. A human being could carry out steps essentially corresponding to what is recited in the independent claims. A human being could first obtain a description of a transaction object from a second human being, the administrator. The first human being could then obtain a guideline related to a summary of the description from a third human being, a written source, etc. The first human being could send the description and the guideline to a summarization model to obtain a summary of the description in accordance with the guideline. The action of sending need not be technological, or could be insignificant extra-solution activity, even if the summarization model involves technology, which is not explicit, as the summarization model could be within the mind of a fourth human being. (Step 2A, Prong One: YES for claims 1-9)
Proceeding to Step 2A, Prong Two, the claims do not recite improvement to the functioning a computer, or to any other technology or technical field. The claims do not contain any other limitations indicative of specific reasons for integrations into a practical application. The claims do not otherwise apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and thus do not make the claims as a whole more than a drafting effort to monopolize the exception. (Step 2A, Prong Two: NO for claims 1-9)
Next, under Step 2B of the Alice/Mayo test, the claims are analyzed to determine whether there are additional claim limitations that individually, or as an ordered combination, ensure that the claims amount to significantly more than the abstract idea. See MPEP 2106.05. Analysis under Step 2B is largely parallel to analysis under Step 2A, Prong Two, and leads to the same conclusions, with the additional question of whether the claims add a specific limitation other than what is well-understood, routine, conventional activity in the field. First addressing independent claim 1, Barreto et al. (U.S. Patent Application Publication 2022/0383346) discloses (paragraph 20, emphasis added), “The online service server can include conventional computing systems, such as server computers, server clusters, cloud storage or computing capability, or other suitable systems.” Hence, the one or multiple servers of claims 1-7 require only the use of well-understood, routine, and conventional technology. Since servers are computers, this also applies to claim 8, which refers to “one or multiple computers”.
The courts have recognized storing and retrieving information in memory as well-understood, routine, and conventional functions, in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d at 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1363, 115 USPQ2d at 1092-93 (Fed. Cir. 2015). Therefore, “to obtain a guideline related to a summary of the description from a memory”, as recited in claim 1, requires only the use of well-understood, routine, and conventional technology.
The courts have recognized the following computer functions as well-understood, routine and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090,1093 (Fed. Cir. 2015) sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Hence, “to transmit the description and the guideline to a summarization model to a summarization model”, as recited in claim 1, requires only the use of well-understood, routine, and conventional technology. Therefore, the limitations of claim 1, whether considered separately or in combination, do not raise the claimed invention to significantly more than an abstract idea.
Furthermore, claims 2 through 7, each of which depends from claim 1, do not recite any significant technology not already present in claim 1. (Claim 5 recites “transmitting the guideline to an evaluation model along with the description and the summary”, but this requires only the use of well-understood, routine, and conventional technology, as set forth above with regard to the transmitting operation in claim 1.) Therefore, the limitations of claims 2, 3, 4, 5, 6, and 7, whether considered separately or in combination with the limitations of claim 1, do not raise the claimed invention to significantly more than an abstract idea.
Independent claim 8 is broader than claim 1, and therefore the limitations of claim 8, whether considered separately or in combination with each other, do not raise the claimed invention to significantly more than an abstract idea, for at least the reasons set forth above with respect to claim 1.
Independent claim 9 recites “A non-transitory computer readable medium storing a program for causing one or multiple computers to” [perform operations corresponding to those of method claim 8]. Avidan et al. (U.S. Patent Application Publication 2017/0193592) discloses (paragraph 25, emphasis added), “Although not illustrated, it should be appreciated that the ecommerce server 110, the merchant computer 120, and the customer computer 130 each include conventional components, such as a processor and a memory medium storing computer-readable instructions that are executable by the processor to perform various operations including those described herein. The computer-readable instructions can be stored on non-transitory computer-readable storage media of a conventional type, whether devices and/or materials.” Hence, the non-transitory computer readable medium storing a program for causing one or multiple computers to perform operations requires only the use of well-understood, routine, and conventional technology. Therefore, the limitations of claim 1, whether considered separately or in combination, do not raise the claimed invention to significantly more than an abstract idea. (Step 2B: NO for claims 1-9)
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.
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.
Claims 1, 2, 4, 6, 8, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Peddinti (U.S. Patent Application Publication 2025/0124476) in view of Unnikrishnan et al. (U.S. Patent Application Publication 2025/020035) and Zhang (U.S. Patent Application Publication 2024/0311569). As per claim 8, Peddinti discloses obtaining a guideline relating to a summary (paragraph 47, emphasis added), “The prompt generation module 218 may generate the prompt based on the reviews to be summarized, information associated with a customer (e.g., a request received from a customer client device 100 associated with the customer for information describing the set of items, contextual information associated with the customer, customer data associated with the customer, etc.), or based on any other suitable types of information.” Peddinti then discloses (paragraph 48, emphasis added), “The prompt further may include guidelines for summarizing the set of reviews for each item (e.g., to limit each summarized review to a character or word limit, to include and highlight words or brands that are likely to be relevant to the customer, to prioritize words that appear most frequently and that are also unique to each corresponding item, etc.).” Note also the final sentence of paragraph 50 of Peddinti. Peddinti further discloses one or multiple computers, as may be seen in Figure 1, and described thus (paragraph 11, emphasis added), “The customer client device 100 is a client device through which a customer may interact with the picker client device 110, the retailer computing system 120, or the online system 140. The customer client device 100 can be a personal or mobile computing device, such as a smartphone, a tablet, a laptop computer, or a desktop computer.”
Peddinti does not disclose obtaining a description of a transaction object input by an administrator of the transaction object. It is noted, however, that Peddinti discloses items available for sale, which are therefore transaction objects (e.g., paragraph 28, emphasis added), “The item data may further include purchasing rules associated with each item, if they exist. For example, age-restricted items such as alcohol and tobacco are flagged according to the item data. Item data may also include information that is useful for predicting the availability of items at retailer locations. For example, for each item-retailer combination (a particular item at a particular retailer location), the item data may include 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, or the popularity of the item. Item data may also include reviews for items provided by customers or other users, as described below.” Unnikrishnan teaches (paragraph 170, emphasis added), “At operation 1040, one or more items are identified as relevant to the processed query, and descriptions of each of the identified items are obtained. The descriptions can be obtained from websites offering products relevant to the item description in the processed query.” This implies the existence of descriptions of items having been inputted or otherwise made available at the websites, and the person who did the inputting can be considered an administrator of the item or of the website/online retailer. Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ earliest priority to obtain a description of a transaction object input by an administrator of the transaction object, for the obvious advantage of basing a summary or other set of information on the available and relevant data.
Peddinti does not disclose sending the description and the guideline to a summarization model to obtain a summary of the description in accordance with the guideline, but Zhang teaches sending a query including guidance and/or terms relevant to a description to a summarization model to obtain a summary (what is received at one end is sent from the other) (paragraph 28, emphasis added), “At 302, a query from a user is received, by, for example, the machine learning summarization platform 102. At 304, the inference model 118 determines an inference of the query 116 by, for example, using NLP techniques. For example, the inference model 118 may use keyword analysis, user location, search history, tokenization and/or semantic analysis to determine a most likely inference or meaning (or intention of the user) behind the query 116. In the examples described herein, either the query 116 or the determined inference results in a request for a summary of a website based at least on a particular topic identified from the query 116”. Zhang further teaches (paragraph 46, emphasis added), “An example computerized method comprises: receiving a query from a user; based on the query, selecting a website: identifying a plurality of media on the website: based at least on the query, selecting a portion of the plurality of media on the website: extracting content from each piece of media from the selected portion of the plurality of media based on the query: generating semantic summaries of the extracted content; aggregating the semantic summaries; and providing the aggregated semantic summaries.” Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ earliest priority to send the description and the guideline to a summarization model to obtain a summary of the description in accordance with the guideline, for the obvious advantage of obtaining a summary of the description with such advantages disclosed by Peddinti [see paragraph 50] as having a length that does not exceed a character or word limit [and thus being capable of being read more quickly], including and highlighting words or brands that are likely to be relevant to a customer, and prioritizing words that appear most frequently and that are also unique to the item [and thus emphasizing what is special about a particular item].
As per claim 9, this recites a non-transitory computer storing a program for causing one or more computers to perform operations parallel to the method steps of claim 8, and is therefore obvious over Peddinti (U.S. Patent Application Publication 2025/0124476) in view of Unnikrishnan et al. (U.S. Patent Application Publication 2025/020035) and Zhang (U.S. Patent Application Publication 2024/0311569), largely as set forth above with respect to claim 8. Additionally, Peddinti further teaches (paragraph 66, emphasis added), “For example, the data store 240 may store the set of parameters for a trained machine learning model on one or more non-transitory, computer-readable media.” Further, Unnikrishnan teaches (paragraph 194, emphasis added), “A non-transitory storage medium is any available medium that can be accessed by a computer. For example, non-transitory storage medium can comprise random access storage media (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EE-PROM), compact disk or other optical disk storage, or any other non-transitory storage medium for carrying or storing data or code.”
As per claim 1, this recites an information processing system comprising one or multiple servers, wherein at least one of the one or multiple servers is configured to perform operations parallel to the method steps of claim 8, and is therefore obvious over Peddinti (U.S. Patent Application Publication 2025/0124476) in view of Unnikrishnan et al. (U.S. Patent Application Publication 2025/020035) and Zhang (U.S. Patent Application Publication 2024/0311569), largely as set forth above with respect to claim 8. In addition, Unnikrishnan teaches servers (paragraph 39, emphasis added), “Some large cloud networks have functions distributed over multiple locations from central servers [note the plural]. A server is designated an edge server if the server has a direct or close connection to a user.” Unnikrishnan further teaches (paragraph 44, emphasis added), “In various embodiments, the summarization system 120 is implemented on a server. A server provides one or more functions to users linked by way of one or more networks. In some cases, the server can include a single microprocessor board, which includes a microprocessor responsible for controlling aspects of the server. In some cases, a server uses on or more [sic, presumably an error for ‘one or more’] microprocessors and protocols to exchange data with other devices/users on or more of the networks via hypertext transfer protocol (HTTP), and simple mail transfer protocol (SMTP), although other protocols such as file transfer protocol (FTP), and simple network management protocol (SNMP) are used. In some cases, a server is configured to send and receive hypertext markup language (HTML) formatted files (e.g., for displaying web pages). In various embodiments, a server comprises a general-purpose computing device, a personal computer, a laptop computer, a mainframe computer, a supercomputer, or any other suitable processing apparatus.” Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ earliest priority to have a system comprising one or more multiple servers, configured as recited, for the obvious advantage of readily enabling interaction between different personal computers, website computers, etc., seeking or possessing different information, programs, etc.
As per claim 2, Peddinti then discloses a guideline including one or more selection rules for selecting content that should be retained in the summary from a description (paragraph 48, emphasis added), “The prompt further may include guidelines for summarizing the set of reviews for each item (e.g., to limit each summarized review to a character or word limit, to include and highlight words or brands that are likely to be relevant to the customer, to prioritize words that appear most frequently and that are also unique to each corresponding item, etc.).”
As per claim 4, Peddinti does not disclose that the guideline includes a modification rule that specifies a modification method for modifying a textual expression in the description, but Unnikrishnan teaches a user providing guidelines including a modification rule or rules for modifying textual expression(s) in a summary (paragraph 64, emphasis added), “At operation 320, a user can provide a query regarding an item of interest to the summarization system 120, where the query can be a natural language query for identifying items of interest to the user 105. The natural language query can be text and vocabulary formatted as a user would speak, write, or otherwise use normally. The user can provide a summarization type that indicates the user’s preference of an emphasis on item similarities or item differences in the summarization. The summarization type can include a compression parameter to adjust the conciseness of the generated summarization. The generated summarization can be a comparative summarization that suppresses repeated information in the descriptions and accentuates the similarities and difference between items.” Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ earliest priority for the guideline to include a modification rule that specifies a modification method for modifying a textual expression in the description, for the obvious advantage, as per Unnikrishnan, of having the summary be adjusted and modified in accordance with a user’s preferences and interests.
As per claim 6, Peddinti does not disclose providing the summary such that the summary is viewable for users by each prescribed item, but Unnikrishnan teaches providing summaries to users (paragraph 57, emphasis added), “The summary list and summarization of information is generated and provided to the user by the summarization component 240 from the information obtained by the search component 250.” Unnikrishnan further teaches (paragraph 100, emphasis added), “In various embodiments, the initial summary of the search results 420 is communicated to the user device 110 and presented to the user 105.” Unnikrishnan yet further teaches (paragraph 153, emphasis added), “The summary based on the similarities and differences is presented to a user.” This would be essentially pointless and meaningless if the summary or summaries were not viewable by the user or users for items of interest to the user or users. Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ earliest priority for at least one of the one or multiple servers to provide the summary such that the summary is viewable for users by each prescribed item, for the obvious advantage of making the system of practical use to potential users.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Peddinti (U.S. Patent Application Publication 2025/0124476) in view of Unnikrishnan et al. (U.S. Patent Application Publication 2025/020035) and Zhang (U.S. Patent Application Publication 2024/0311569) as applied to claim 1 above, and further in view of Wong et al. (U.S. Patent Application Publication 2023/0316004). Wong teaches translating a text from one language into a different language, and outputting text (paragraph 26, emphasis added), “In a fifth aspect, there is provided a computer-implemented method of natural language translation, comprising: receiving text in a first language (or source language); processing the received text using the natural language translation model of the second aspect; and outputting text in a second language (or target language) based on the processing, the text in the second language being a translation of the text in the first language.” Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ earliest priority for at least one of the one or multiple servers to be configured to provide a translated text obtained by translating the summary into a different language such that the translated text is viewable for users, for at least the obvious advantage of facilitating service for a user who does not know the language of the summary, or at least has facility with that language than with another and preferred language.
Non-Obvious Subject Matter
Claim 3 is rejected under 35 U.S.C. 101, objected to for an informality (in claim 1), and objected to as depending from a claim rejected for obviousness, but recites non-obvious subject matter.
The following is a statement of reasons for the indication of non-obvious subject matter: The closest prior art of record, Peddinti (U.S. Patent Application Publication 2025/0124476), discloses elements of claim 1, as set forth above, with other elements of claim 1 taught by Unnikrishnan et al. (U.S. Patent Application Publication 2025/020035) and Zhang (U.S. Patent Application Publication 2024/0311569), also as set forth above. However, Peddinti does not disclose that the guideline includes an ordering rule that specifies an arrangement order of multiple sentences included in the summary, and no other prior of record supplies this deficiency. Abishek Kumar et al. (U.S. Patent Application Publication 2021/0232943) discloses ranking of sentences (see paragraphs 5 and 6), but does not sufficiently teach what is recited in claim 3.
Claim 5 is rejected under 35 U.S.C. 101, objected to for an informality (in claim 1), and objected to as depending from a claim rejected for obviousness, but recites non-obvious subject matter.
The following is a statement of reasons for the indication of non-obvious subject matter: The closest prior art of record, Peddinti (U.S. Patent Application Publication 2025/0124476), discloses elements of claim 1, as set forth above, with other elements of claim 1 taught by Unnikrishnan et al. (U.S. Patent Application Publication 2025/020035) and Zhang (U.S. Patent Application Publication 2024/0311569), also as set forth above. However, Peddinti does not disclose that at least one of the one or multiple servers is configured to evaluate whether the summary has been generated in accordance with the guideline by transmitting the guideline to an evaluation model along with the description and the summary, and no other prior of record supplies this deficiency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Baran et al. (U.S. Patent 8,019,656) disclose a system and method for generating an alternative product recommendation. Abishek Kumar et al. (U.S. Patent 11,562,264) disclose a system and method for using machine learning to select one or more submissions from a plurality of submissions. Hwang (U.S. Patent 11,574,116) discloses an apparatus and method for providing summarized information using an artificial intelligence model. Sabapathy et al. (U.S. Patent 12,361,228) disclose natural language processing techniques for machine-learning-guided summarization using hybrid class templates. Unnikrishnan et al. (U.S. Patent 12,469,068) is the U.S. Patent issued on the application previously published as U.S. Patent Application Publication 2025/0200635, and used in making rejections under 35 U.S.C. 103. Ramanujam (U.S. Patent 12,475,324) discloses an artificial intelligence-enabled system and method for authoring a scientific document (corresponding to a patent application publication on an IDS). Wong et al. (U.S. Patent 12,475,329) is the U.S. Patent issued on the application previously published as U.S. Patent Application Publication 2023/0316004, and used in making a rejection under 35 U.S.C. 103. Kim et al. (U.S. Patent 12,591,607) recites automated content creation and content services for collaboration platforms (corresponding to a patent application publication on an IDS).
Bantz et al. (U.S. Patent Application Publication 2005/0246434) disclose services for capturing and modeling computer usage. Baran et al. (U.S. Patent Application Publication 2008/0215349) disclose a system and method for generating an alternative product recommendation. Abishek Kumar et al. (U.S. Patent Application Publication 2021/023943) disclose a system and method for using machine learning to select one or more submissions from a plurality of submissions. Sabapathy et al. (U.S. Patent Application Publication 2023/0385557) disclose natural language processing techniques for machine-learning-guided summarization using hybrid class templates. Mateos et al. (U.S. Patent Application Publication 2025/0217578) has been considered for possible double patenting (rejections not made).
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/NICHOLAS D ROSEN/ Primary Examiner, Art Unit 3689 June 11, 2026
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