DETAILED ACTION
The following NON-FINAL Office Action is in response to Applicant’s Remarks filed on 12/17/2025.
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 . 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 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/17/2025 has been entered.
Status of Claims
Claims 1-2, 5-7, 10-11, and 16-19 were previously pending and subject to a final Office Action mailed 09/23/2025. Claims 1 and 16-17 were amended. Claims 1-2, 5-7, 10-11, and 16-19 are currently pending and are subject to the non-final Office Action below.
Priority
Examiner has noted that the Applicant has claimed priority from the foreign application DKPA202170044 filed on 01/29/2021 and PCT/EP2022/052048 filed on 01/28/2022.
Response to Arguments
35 USC § 101
Applicant’s arguments, see pages 6-15, filed 12/17/2025, with respect to the 35 U.S.C. 101 rejections of Claims 1-2, 5-7, 10-11, and 16-19 have been fully considered and are not persuasive.
Applicant argues on pages 7-8 that the amended claim 1 requires a specific computational arrangement and specific operations which are technical considerations not directed to certain methods of organizing human activity.
Examiner respectfully disagrees as a user providing input to modify their booking data is a commercial interaction, or business relation between the customer and shipper. “Interpreting unstructured text data to modify stored electronic data” is directed to the abstract idea of interpreting the customer’s input to determine how to modify the customer’s booking. Even when considered as a whole, the “specific computational arrangement” is merely an additional element utilized to perform the abstract idea.
Further MPEP 2106.04(a)(2)(II) states “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 “specific computational arrangement” as an additional element is addressed in later arguments.
Applicant argues on page 8-9 that the eligibility of the claims is self-evident as claim 1 recites specific operations to improve the performance of a computer in automatically processing modifying data. Examiner respectfully disagrees.
Applicant’s claim allows for a more accurate interpretation of the customer’s input which is an improvement in the process of analyzing the user input of unstructured text data set in order to modify the user’s booking. Thus, it is an improvement in the abstract idea of business relations as customers wish to quickly update the shipper and have their shipment prioritized (Applicant’s Background).
See MPEP 2106.05(a)(II) states “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
Applicant argues on pages 10-12 that the claim does not recite a method for organizing human activity. Examiner respectfully disagrees.
“Processing unstructured text data” is directed to the abstract idea of “certain methods of organizing human activity” specifically commercial interactions or business relations as evident by Applicant’s background “booking modifications…are part of the useful services offered to customers”.
The unstructured text data is provided by a customer to the “computer” which “automatically and reliably interpret and process unstructured text data in order to modify stored electronic data” as a service. The “computational specifics” or “computation components” were not ignored, they were analyzed and determined as additional elements which perform the abstract idea.
Applicant argues on pages 12-14 regarding prong two that the additional elements are not “apply it” or field of use and that claim 1 provides a solution to a technical problem. Examiner respectfully disagrees. First, the additional elements of the NLP model, tokenizer, and tagger may not be “apply it” but they are still considered field of use and additionally, extra-solution activity.
The application of the abstract idea is limited to a specific type of data – for example, “identify an entity pattern” is limited to data that has been pre-processed, tokenized, and tagged.
Further, specifying that the abstract idea relates to activities that are executed in a natural language processing environment (to execution by a NLP model that has been trained and performs NLP techniques such as tokenizing and tagging).
Further, it is unclear how Applicant’s specific components and operations provide an improvement in how the computer functions to process unstructured data reliably. Also, such improvements are not improvements to computer capabilities or an improvement to an existing technology, but rather, improvements to the abstract idea as Examiner noted previously.
Applicant argues on pages 14-15 that Claim 1 amounts to significantly more because many of the features are not well-understood, routine, and conventional in the technological field of electronic data conflict identification. Examiner respectfully disagrees as even when viewed as an ordered combination, the entity extraction model utilizes natural language processing techniques which are extra-solution activity that is well-understood, routine, and conventional in the art. See rejection below for analysis of the entity extraction model.
Accordingly, the 35 U.S.C. 101 rejection is maintained.
35 USC § 103
Applicant’s arguments on page 15-16 of Applicant’s Remarks with respect to the 35 U.S.C. 103 rejection have been fully considered and are persuasive. Accordingly, the 35 U.S.C. 103 rejection is withdrawn.
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 1-2, 5-7, 10-11, and 16-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-2, 5-7, 10-11, and 16-19 are directed to a method (i.e., a process). Therefore, the claims all fall within one of the four statutory categories of invention.
Step 2A Prong 1
Independent claim 1 recites the limitations of:
inputting the pre-processed text data set …to determine a modification set comprising an entity parameter and a first modification parameter based on the pre-processed text data set, wherein: the entity parameter is selected from a list of targeted entities, the list of targeted entities comprising one or more entities indicative of a vessel, a vessel type, a vessel name, equipment, a voyage, a service, or an environmental parameter;
… identify an entity pattern associated with entities of the list of targeted entities based on the learned associative patterns, the tokenized document, and the applied tags, wherein the entity parameter is determined based on the identified entity pattern,
…. determine a first confidence parameter associated with the first modification parameter;
outputting, based on the modification set, a modification output for modifying the booking data;
comparing the modification output to a relation complexity threshold, the relation complexity threshold based on historical booking data of the booking system; and
modifying, based on the modification output satisfying a relation complexity threshold criterion, a booking associated with the entity parameter with the first modification parameter
The limitations of Claim 1 stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (“managing personal behavior or relationships or interactions between people” or “commercial interactions”). Specifically, commercial interactions or business relations between a customer and a shipper.
The “Background” section on page 1 of Applicant’s specification details that “booking modifications, such as booking amendments, are part of the useful services offered to customers. Customers may want a shipper to prioritize a shipment and may wish the shipper to be updated at the earliest. Any delay in handling the booking modification not only impacts the customer satisfaction, but also leads to obstacles such as pending queues to be processed by resources, and possible delay in settlement”. Thus, it is further evident that the claims are directed to organizing business relations between a shipper and a customer. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. The independent claim recites the additional elements of an electronic device, the limitations of “receiving” and “pre-processing”, and the limitations regarding an entity extraction model.
The additional element of an electronic device is recited at a high-level of generality (generic computer/functions), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components. See MPEP 2106.05(f) “Mere Instructions to Apply an Exception”.
The limitation of “receiving, by the electronic device and from a user, user input comprising an unstructured text data set associated with a booking for a shipping system” amounts to extra-solution activity specifically pre-solution activity of data gathering.
The limitation of “pre-processing the unstructured text data set to reduce noise in the unstructured text data set to obtain a pre-processed text data set” amount to extra-solution activity specifically “selecting a particular data source or type of data to be manipulated”.
The limitations of “the entity extraction model comprises a natural language processing (NLP) model, the NLP model comprising: a tokenizer configured to divide the pre-processed text data into tokens to generate a tokenized document; and a tagger configured to apply tags to elements of the tokenized documents to categorize the elements” amount to extra-solution activity, specifically “selecting a particular data source or type of data to be manipulated” similar to “selecting information, based on types of information and availability of information in a power grid environment for collection, analysis, and display”. See MPEP 2106.05(g).
The limitations noted above (receiving, pre-processing, the entity extraction model) and additionally, the limitation of “an entity extraction model, the entity extraction model trained on a booking corpus comprising user defined notes for booking modifications to learn associative patterns between token structures and entity types using a vocabulary” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use which does not meaningfully limit the claim.
The data gathering step is limited to particular data source or a particular type of data. Additionally, the limitations are further limiting application of the abstract idea to a specific type of data and limiting the claims to the natural language processing field. Specifically limiting the abstract idea of “identify an entity pattern” to data that has been pre-processed, tokenized, and tagged and limiting the execution of the abstract idea to “an entity extraction model” which has been trained and comprises a NLP model comprising a tokenizer and a tagger. See MPEP 2106.05(h).
Thus, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application as the additional elements are mere instructions to apply the judicial exception using generic computer components, extra-solution activity, and field of use which does not impose meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The claim 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 of an electronic device to perform the steps recited above amounts to no more than mere instructions to apply the exception using a generic computer.
The limitation of “receiving” which amounts to pre-solution activity of data gathering is similar to the computer function of “receiving or transmitting data over a network” which the courts have recognized as well-understood, routine, and conventional when claimed as insignificant extra-solution activity. See MPEP 2106.05(d).
The limitations of “pre-processing” and “the entity extraction model” amount to extra solution activity.
Regarding “pre-processing”, page 8 lines 10-17 (or paragraph 28 of the PGPub US2024/0086781) of Applicant’s specification states “Text data set 11 may be fed into a step 12 related to a pre-processing step, such as text pre-processing technique. Any number of pre-processing steps can be performed. For example the step 12 may provide the text data with a reduced noise to a pattern identification 14. The step 14 provides one or more possible or potential pairs of entity parameter and modification parameter to a standardisation step 16 for standardizing and/or normalizing the one or more possible (e.g. pairs of) the entity parameter and the first modification parameter.”
Thus, the specification discloses pre-processing unstructured text may be accomplished through a variety of techniques such as reducing noise, normalizing, or transforming the unstructured text which demonstrates that pre-processing of unstructured text is well-understood, routine, and conventional as pre-processing data before analyzing it is a common procedure in the art.
Regarding the “the entity extraction model” limitation, page 10-11 lines 26-2 (or paragraph 64 of the PGPub) states “In one or more example methods, the determining S104 comprises tokenizing S104B and/or applying S104C a tag to a corresponding element of the text data set based on the entity extraction model. Tokenization may be seen as a technique of separating a piece of text into smaller units called tokens. Here, tokens can be either words, characters, or subwords. Hence, tokenization can be classified into 3 types—word, character, and subword (n-gram characters) tokenization. For example, tokenization may break bigger pieces of text into its respective elements like single words (unigram), and/or two consecutive words (bi-gram)”; page 7 Lines 14-30 (or paragraph 40-43 of the PGPub) “The representation 3 of the entity extraction model comprises elements for tagging such as one or more of: a tagger 50, a text categorizer 51, a custom component 53, a dependency parser 55, and an entity recognizer 57”; and “The extraction model may use one or more elements of tagging (50, 51, 53, 55, 57) to determine if 20 inch is an equipment or something else: and to identify which category this text belongs to.”
Thus, the entity extraction model (tokenizer and tagger) limitations are described in the specification in a way that shows they are widely prevalent and/or in common use as there are multiple types of tokenization used in NLP in order to segment text for analysis and tagging is so well-known that it need not be described in detail in the patent specification. See MPEP 2106.05(d).
Again, the limitations noted above (receiving, pre-processing, the entity extraction model) and additionally, “an entity extraction model, the entity extraction model trained on a booking corpus comprising user defined notes for booking modifications to learn associative patterns between token structures and entity types using a vocabulary” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use.
The data gathering step is limited to particular data source or a particular type of data. Additionally, the limitations are further limiting application of the abstract idea to a specific type of data and limiting the claims to the natural language processing field. Specifically limiting the abstract idea of “identify an entity pattern” to data that has been pre-processed, tokenized, and tagged and limiting the execution of the abstract idea to “an entity extraction model” which has been trained and comprises a NLP model comprising a tokenizer and a tagger. See MPEP 2106.05(h).
Mere instructions to apply an exception using a generic computer component, extra-solution activity, and field of use cannot provide an inventive concept. The claim is not patent eligible.
None of the steps of Claim 1 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to organizing human activity, extra-solution activity, field of use, and mere instructions to apply an exception using generic computer components, thus, the analysis does not change when considered as an ordered combination. The additional elements do not meaningfully limit the claim. Accordingly, Claim 1 is ineligible.
Regarding dependent Claims 10-11, and 16-17
The limitations are part of the abstract idea of organizing human activity as they merely specify determining a language of the text data set (claim 11), what evaluating the modification output against a threshold involves (claim 10), and what comparing the confidence parameter to a criterion involves (claim 16 and claim 17).
Regarding dependent Claims 2, 5, and 6-7
Claim 2, Claim 5, Claim 6, and Claim 7 involve limitations which amount to extra solution activity such as pre-solution data gathering and/or selecting a particular data source or type of data to be manipulated and field of use (limiting the application of the abstract idea to pre-processed text data sets).
Extra-solution activity and field of use limitations do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Pre-solution activity of data gathering is similar to the computer function of “receiving or transmitting data over a network” which the courts have recognized as well-understood, routine, and conventional when claimed as insignificant extra-solution activity. See MPEP 2106.05(d).
Further, pages 8-9 of Applicant’s specification disclose pre-processing unstructured text may be accomplished through a variety of techniques such as reducing noise, normalizing, or transforming the unstructured text which demonstrates that pre-processing of unstructured text is well-understood, routine, and conventional as pre-processing data before analyzing it is a common procedure in the art.
Regarding dependent Claims 18-19
Claim 18 and Claim 19 add the additional elements of memory circuitry, processor circuitry, an interface, an electronic device, a computer readable storage medium, and one or more programs. Such elements are recited at a high-level of generality (generic computer/functions), such that, when viewed as whole/ordered combination, they amounts to no more than mere instruction to apply the judicial exception using generic computer components. See MPEP 2106.05(f) “Mere Instructions to Apply an Exception”.
Thus, taken alone and when viewed in combination, the dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Nothing in dependent claims 2, 5-7, 10-11, and 16-19 adds additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-2, 5-7, 10-11, and 16-19 are ineligible.
Closest Prior Art
Examiner noting that the claims are rejected under 35 U.S.C. 101.
The following is a statement of reasons for the indication of closest prior art: Examiner is unaware of any combination of available prior art which teaches or suggests the limitations within the independent claim in a manner in which it is obvious to combine the references.
The following are the closest prior art:
Brown et al. (US2012/0246081) teaches limitations such as receiving, by the electronic device and from a user, user input comprising a text data set associated with a booking for a shipping system; the entity extraction model configured to determine a modification set comprising an entity parameter and a first modification parameter based on the text data set; wherein the entity parameter is selected from a list of targeted entities, the list of targeted entities comprising one or more entities indicative of a vessel, a vessel type, a vessel name, equipment, a voyage, a service, or an environment parameter; the entity extraction model comprises a Natural Language Processing (NLP) model; outputting, based on the modification set, a modification output for modifying the booking data; comparing the modification output to a relation complexity threshold; and modifying, based on the modification output satisfying a relation complexity threshold criterion, a booking associated with the entity parameter with the modification parameter.
Li et al. (US Patent No. 11,341,339) teaches limitations such as the unstructured text data set; inputting the text data set into an entity extraction model; configured to identify an entity pattern associated with entities of the booking system; and the NLP configured to determine a first confidence parameter associated with modification parameter.
Fleischman et al. (US2014/0095524) teaches the threshold based on historical booking data of the booking system.
Moon et al. (US2020/0410012) teaches the NLP model comprising a tokenizer and a tagger.
Hakkani-Tur et al. (US2021/0217408) teaches the NLP model comprising a tokenizer and tagger and further teaches where the model trained to receive a user utterance, tokenize it, and output representations. See para. 34 “For example, the input phrase 126 “Hello, book me a table for two at Cascal” would be tokenized in the following format [“Hello”, “book”, “me”, “a”, “table”, “for”, “two”, “at”, “Cascal”].)”
Sandor et al. (US2017/0286396) teaches training a model on a corpus of forum posts to learn patterns in information requests.
Khemka et al. (US2023/0409615) teaches receiving user input, processing the user input using a natural language understanding module, and providing a response to the user input. The system may perform concierge type tasks such as making dinner reservations or travel arrangements. Khemka is not available as prior art due to the effective filing date.
Conclusion
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/L.M./Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628