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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Drawings
The drawings filed 8/18/2023 were accepted.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are:
"a communication module configured to perform" in claims 1 and 2;
“an analytics module configured to perform” in claim 1; and
“a training module configured to perform” in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 USC 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because they are directed to an abstract idea without significantly more. The claims recite the abstract idea of receiving selections (receiving a first selected word tile from the first set of word tiles… receiving a second selected word tile from the second set of word tiles), generating a phrase (generating a phrase based on the first selected word tile and the second selected word tile), accessing and adding a word tile (accessing the first selected word tile from the first set of word tiles, adding the first selected word tile to a set of selections of a plurality of selections), and determining a second set of word tiles (determining the second set of word tiles).
Step 2A, Prong 1
The limitations that describe the receiving selections, generating a phrase, accessing and adding a word tile, and determining a second set of word tiles are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The claims also include elements of modules that perform operations and presenting word tiles (presenting a first set of word tiles for selection… presenting a second set of word tiles based on the first selected word tile from the first set of word tiles), however nothing in the claims precludes the steps from practically being performed in the mind.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application because the additional elements regarding performing operations and presenting word tiles are considered insignificant extra-solution activity. These limitations are not considered improvements to the functioning of a technology or technical field. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the extrasolutionary elements are not considered significantly more than just applying the steps of receiving selections, generating a phrase, accessing and adding a word tile, and determining a second set of word tiles.
Step 2B
In addition to the abstract idea, the claims have the performing operations and presenting word tiles, but they represent only well-understood, routine, conventional activity that can be performed on generic computers. The performing of operations by the modules are considered as merely applying the abstract ideas. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Ly Tan et al (hereinafter L1; US20210390881A1; filed 10/21/2019; published 12/16/2021) discloses how well-understood, routine, and conventional the presenting of tiles is: abstract: “a user interface to display word tiles for selection.” The claims are not patent eligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 2, this claim recites an additional abstract idea of identifying a word tree. The providing of an identification is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. There are no other additional elements.
As per claim 3, this claim has similar presenting steps and is rejected similarly to claim 1.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 4, this claim recites an additional abstract idea of identifying a word tree. The providing of an identification is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. There are no other additional elements.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 5, this claim recites an additional abstract idea of generating a sentence. The generating a sentence is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. This claim also recites an additional element of providing an input to a trained model.
(Step 2A, prong 2) The judicial exception is not integrated into a practical application because the additional elements regarding providing an input to a trained model are considered insignificant extra-solution activity. These limitations are not considered improvements to the functioning of a technology or technical field.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the providing an input to a trained model is not considered significantly more than the judicial exception. The sending and receiving of data has been recognized by the courts as being 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. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) and MPEP 2106.05(d), subsection II. The claims are not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 6, this claim recites an additional abstract idea of generating communication data. The generating of data is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. There are no other additional elements.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 7, this claim recites additional abstract ideas of predicting a likelihood and prioritizing tiles. The predicting and prioritizing are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind. There are no other additional elements.
As per claim 8, this claim has similar predicting steps and is rejected similarly to claim 7.
As per claim 9, this claim has similar predicting steps and is rejected similarly to claim 7.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 10, this claim recites an additional abstract idea of generating a likelihood. The generating a likelihood is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. This claim also recites an additional element of providing an input to a trained NLP model.
(Step 2A, prong 2) The judicial exception is not integrated into a practical application because the additional elements regarding providing an input to a trained model are considered insignificant extra-solution activity. These limitations are not considered improvements to the functioning of a technology or technical field.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the providing an input to a trained model is not considered significantly more than the judicial exception. The sending and receiving of data has been recognized by the courts as being 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. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) and MPEP 2106.05(d), subsection II. The claims are not patent eligible.
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 11, this claim recites additional abstract ideas of collecting data and increasing a delay time. The collecting and increasing are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind. There are no other additional elements.
As per claim 12, this claim has similar collecting interaction data steps and is rejected similarly to claim 11.
Claims 13-15 recite substantially similar limitations to claims 1-3 respectively and are thus rejected along the same rationales.
Claim 16 recites substantially similar limitations to claim 7 and is thus rejected along the same rationale.
Claims 17-19 recite substantially similar limitations to claims 1-3 respectively and are thus rejected along the same rationales.
Claim 20 recites substantially similar limitations to claim 7 and is thus rejected along the same rationale.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 5-6, 11-12, 13, and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ly Tan et al (hereinafter L1; US20210390881A1; filed 10/21/2019; published 12/16/2021).
With regards to claim 1, L1 discloses A device for assistive communication (L1, abstract: “A communication device includes a communication module to generate a user interface to display word tiles for selection, compile a sentence upon selection of word tiles, and output the sentence”), comprising:
a communication module configured to perform operations comprising presenting a first set of word tiles for selection, receiving a first selected word tile from the first set of word tiles (L1, paragraph 33: “Selection of a word tile may provide for the selection of additional selection of word tiles so that the user may select several words to be compiled into a sentence for output. For example, selection of a first word tile 302 may generate a group 304 of additional word tiles, and selection of a word tile from the group 304;” the group of additional word tiles are interpreted as the claimed “first set of word tiles”), presenting a second set of word tiles based on the first selected word tile from the first set of word tiles, receiving a second selected word tile from the second set of word tiles (L1, paragraph 36: “the selection of a particular word tile may cause the dynamic generation of a following group of word tiles, wherein the following group is generated according to a predictive algorithm”), and generating a phrase based on the first selected word tile and the second selected word tile (L1, paragraph 37: “As word tiles are selected, the selected word tiles may be stored in a sentence container 310. As word tiles are included in the sentence container 310, additional sentence structure elements, such as articles, prepositions, or other words and/or punctuation, may be generated and inserted as appropriate into the sentence container 310”); and
an analytics module configured to perform operations comprising accessing the first selected word tile from the first set of word tiles, adding the first selected word tile to a set of selections of a plurality of selections, and determining the second set of word tiles based on the plurality of selections (L1, paragraph 36: “the selection of a particular word tile may cause the dynamic generation of a following group of word tiles, wherein the following group is generated according to a predictive algorithm;” paragraph 33: “selection of a first word tile 302 may generate a group 304 of additional word tiles, and selection of a word tile from the group 304 may generate an additional group 306 of still additional word tiles, the selection of which may generate an additional group 308 of still additional word tiles, and so on”).
With regards to claim 5, which depends on claim 1, L1 discloses wherein generating the phrase comprises providing the first selected word tile and second selected word tile as input to (L1, paragraph 4: “The communication device further includes a training module to collect interaction data, the interaction data including indications of interactions of a user account with the communication module over a plurality of trials”) a machine learning model that is trained to generate a complete sentence based at least in part on sentence fragments (L1, paragraph 36: “the dynamic generation of a following group of word tiles, wherein the following group is generated according to a predictive algorithm. The predictive algorithm may involve presenting the individual with word tiles… A predictive algorithm may include a machine learning algorithm”).
With regards to claim 6, which depends on claim 1, L1 discloses wherein the communication module further comprises generating a communication data for output based on the phrase (L1, paragraph 4: “compile a sentence upon selection of word tiles, and output the sentence”).
With regards to claim 11, which depends on claim 1, L1 discloses a training module configured to perform operations comprising collecting interaction data (L1, paragraph 55: “The progress chart 610 may be generated by interaction data collected as discussed herein, and may be consulted to review the progress of the individual”) and progressively increasing a delay time between receiving a selected word and generating a phrase for output based on the interaction data (L1, paragraph 51: “the audibility of the vocalization of the word may be decreased by, for example, … by delaying output of the sound to provide the user with an opportunity to vocalize the sound themselves… such diminishment may be updated as the user progresses”).
With regards to claim 12, which depends on claim 11, L1 discloses wherein the interaction data comprises interactions by a user with the device over a plurality of instances (L1, paragraph 55: “The progress chart 610 may be generated by interaction data collected as discussed herein, and may be consulted to review the progress of the individual;” fig. 6B: the chart shows interactions over a plurality of days).
Claim 13 recites substantially similar limitations to claim 1 and is thus rejected along the same rationale.
Claim 17 recites substantially similar limitations to claim 1 and is thus rejected along the same rationale.
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.
Claim(s) 2-4, 7-9, 14-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ly Tan et al (L1) in view of Su et al (US20180081964A1; filed 9/22/2016).
With regards to claim 2, which depends on claim 1, L1 does not disclose wherein the communication module is configured to perform operations further comprising, before presenting the first set of word tiles for selection, identifying a word tree for each word tile of the first set of word tiles, wherein each word tree has the corresponding word tile in its first level.
Su et al teaches wherein the communication module is configured to perform operations further comprising, before presenting the first set of word tiles for selection, identifying a word tree for each word tile of the first set of word tiles, wherein each word tree has the corresponding word tile in its first level (Su et al, paragraph 35: “Prefix tree generating unit 508 is configured to generate a prefix tree for each string including the predicted next word. Each letter or symbol of the string is inserted into the prefix tree. The final node of the prefix tree is associated with an inverted index list, containing the suggestion database IDs of the corresponding suggestions;” paragraph 38: “Three prefixes are shown in prefix tree 601 including yahoo s, yahoo se, and app s;” as described in paragraph 38 and shown in fig. 6B, different word tree/suggestions are selected based on the selected word (“yahoo” or “app” in their example); these word trees are indexed prior to any selections made in the current instance).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined L1 and Su et al to use word trees for each word to predict the subsequent word selections. This would have enabled the invention to store and retrieve suggestions based on the current selections of a user (Su et al, paragraph 35: “Storing unit 506 receives the index information, association information, and the prefix trees associated with the suggestions to be stored in suggestion database 108”).
With regards to claim 3, which depends on claim 2, L1 discloses presenting a set of word tiles at a second level… corresponding to the first selected word tile from the first set of word tiles (L1, paragraph 36: “the selection of a particular word tile may cause the dynamic generation of a following group of word tiles, wherein the following group is generated according to a predictive algorithm”).
However, L1 does not disclose a second level of the word tree corresponding to the first selected word.
Su et al teaches a second level of the word tree corresponding to the first selected word (Su et al, paragraph 35: “Prefix tree generating unit 508 is configured to generate a prefix tree for each string including the predicted next word. Each letter or symbol of the string is inserted into the prefix tree. The final node of the prefix tree is associated with an inverted index list, containing the suggestion database IDs of the corresponding suggestions”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined L1 and Su et al to use word trees for each word to predict the subsequent word selections. This would have enabled the invention to store and retrieve suggestions based on the current selections of a user (Su et al, paragraph 35: “Storing unit 506 receives the index information, association information, and the prefix trees associated with the suggestions to be stored in suggestion database 108”).
With regards to claim 4, which depends on claim 2, L1 discloses wherein determining the second set of word tiles for selection comprises… word tiles (L1, paragraph 33: “Selection of a word tile may provide for the selection of additional selection of word tiles so that the user may select several words to be compiled into a sentence for output. For example, selection of a first word tile 302 may generate a group 304 of additional word tiles, and selection of a word tile from the group 304”).
However, L1 does not disclose identifying a word tree having the first selected word… from the first set of word… in its first level and the second selected word… from the second set of word… in its second level.
Su et al teaches identifying a word tree having the first selected word… from the first set of word… in its first level and the second selected word… from the second set of word… in its second level (Su et al, paragraph 35: “Prefix tree generating unit 508 is configured to generate a prefix tree for each string including the predicted next word.” Fig. 6B: The tree shows both the first word (yahoo or app) followed by the indexed suggestions 114).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined L1 and Su et al to use word trees for each word to predict the subsequent word selections. This would have enabled the invention to store and retrieve suggestions based on the current selections of a user (Su et al, paragraph 35: “Storing unit 506 receives the index information, association information, and the prefix trees associated with the suggestions to be stored in suggestion database 108”).
With regards to claim 7, which depends on claim 1, L1 discloses wherein determining the second set of word tiles comprises predicting a likelihood of selection for one or more word tiles of the second set of word tiles based on the plurality of selections (L1, paragraph 36: “the selection of a particular word tile may cause the dynamic generation of a following group of word tiles, wherein the following group is generated according to a predictive algorithm”).
However, L1 does not disclose and prioritizing the one or more word tiles of the second set of word tiles based on the likelihood of selection such that a word tile with the highest likelihood of selection has the highest priority.
Su et al teaches prioritizing the one or more word tiles of the second set of word tiles based on the likelihood of selection such that a word tile with the highest likelihood of selection has the highest priority (Su et al, paragraph 23: “Therefore, for a given context C (i.e., parts of a written statement that precede or follow a specific word or passage) and a next word candidate Wi, the conditional probability P(Wi|C) is also fixed. As all potential candidate probabilities are fixed, the highest ranked candidates can be determined and saved as potential suggestions;” paragraph 32: “Ranking module 310 further ranks the candidates based on the estimated probabilities”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined L1 and Su et al such that the next word tiles are ranked based on the likelihood of the user selecting them. This would have enabled the invention to prune the suggested words based on the likelihood that they are selected (Su et al, paragraph 32: “Filtering module 312 is configured to prune the ranked candidates and select the candidates with the K highest probabilities”).
With regards to claim 8, which depends on claim 7, L1 does not disclose wherein predicting the likelihood of selection is further based on a frequency of one or more word tiles of the second set of word tiles in the plurality of selections.
However, Su et al teaches wherein predicting the likelihood of selection is further based on a frequency of one or more word tiles of the second set of word tiles in the plurality of selections (Su et al, paragraph 32: “Ranking module 310 may apply one or more ranking criteria 316 to estimate the probabilities, such as, the frequency that the candidate is recommended with the same previous word, etc”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined L1 and Su et al such that the next word tiles are ranked based on the likelihood of the user selecting them. This would have enabled the invention to prune the suggested words based on the likelihood that they are selected (Su et al, paragraph 32: “Filtering module 312 is configured to prune the ranked candidates and select the candidates with the K highest probabilities”).
With regards to claim 9, which depends on claim 7, L1 does not disclose wherein predicting the likelihood of selection is further based on a current location.
However, Su et al teaches wherein predicting the likelihood of selection is further based on a current location (Su et al, Paragraph 27: "The ranking criteria may be… the frequency that a suggestion is recommended within a geographic area, etc.").
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined L1 and Su et al such that the next word tiles are ranked based on the likelihood of the user selecting them. This would have enabled the invention to prune the suggested words based on the likelihood that they are selected (Su et al, paragraph 32: “Filtering module 312 is configured to prune the ranked candidates and select the candidates with the K highest probabilities”).
Claims 14-15 recite substantially similar limitations to claims 2-3 respectively and are thus rejected along the same rationales.
Claim 16 recites substantially similar limitations to claim 7 and is thus rejected along the same rationale.
Claims 18-19 recite substantially similar limitations to claims 2-3 respectively and are thus rejected along the same rationales.
Claim 20 recites substantially similar limitations to claim 7 and is thus rejected along the same rationale.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ly Tan et al (L1) in view of Su et al, and further in view of Peleg et al (US20220215164A1; filed 3/24/2022).
With regards to claim 10, which depends on claim 7, L1 discloses wherein predicting the likelihood of selection comprises providing the first selected word tile from the first set of word tiles and one or more word tiles from the second set of word tiles as input to a… model that is trained to generate a… of a word tile from the second set of word tiles following the first selected word tile (L1, paragraph 36: “the selection of a particular word tile may cause the dynamic generation of a following group of word tiles, wherein the following group is generated according to a predictive algorithm”).
However, L1 does not disclose generate a likelihood of a word… from the second set of word… a natural language processing model that is trained… based at least in part on an annotated corpus
Su et al teaches generate a likelihood of a word… from the second set of word (Su et al, paragraph 32: “Ranking module 310 further ranks the candidates based on the estimated probabilities”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined L1 and Su et al such that the next word tiles are ranked based on the likelihood of the user selecting them. This would have enabled the invention to prune the suggested words based on the likelihood that they are selected (Su et al, paragraph 32: “Filtering module 312 is configured to prune the ranked candidates and select the candidates with the K highest probabilities”).
Peleg et al teaches a natural language processing model (Peleg et al, abstract: “generating at least one text output option… causing the at least one text output option to be shown to the user via the display;” paragraph 5: “The disclosed embodiments also include semantically infused language models. Such models may include a neural network-based language model explicitly trained to contain contextual relations between abstract semantic features in text”) that is trained… based at least in part on an annotated corpus (Peleg et al, paragraph 162: “In the supervised step, a dataset of annotated examples may be leveraged to train a model (“Semantic Reader”) on a few Natural Language Understanding tasks which capture semantics (such as Semantic Role Labeling, Semantic Proto-Roles, Coreference, Entity Linking, etc)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined L1, Su et al, and Peleg et al such that the word tiles are generated using a natural language processing model trained on annotated data. This would have enabled the invention to maintain contextual relations between abstract semantic features in text (Peleg et al, paragraph 5: “The disclosed embodiments also include semantically infused language models. Such models may include a neural network-based language model explicitly trained to contain contextual relations between abstract semantic features in text, in contrast with prior art, where models can only be trained to learn contextual relations between surface-level words. For example, the disclosed systems may enable a model to learn contextual relations between words and word senses and between words and the properties of the abstract concepts invoked by the text.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arnold et al (US20180101599A1): Teaches text completions as a user types, which can include multi-word phrases, and uses a trained model.
Baker et al (US20080233546A1): Teaches an AAC tool using selectable word tiles.
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/B.C.A/Examiner, Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178