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
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it repeats information given in the title and exceeds a 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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.
Claim 8 recites a machine readable medium, however, the specification does not define what type of medium is included in the machine readable medium. According to MPEP 2111, the examiner must give the terms or phrases their broadest interpretation definition awarded by one of ordinary skill in the art unless applicant has provided some clear definition of the claimed terms or phrases. Therefore, the examiner interprets the machine readable medium to be any type of medium which includes carrier medium such as signals. Signals are directed to non-statutory subject matter. Thus, claim 8 is rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter because the instant claims include medium that could involve propagation signals, the claims are being held as non-statutory under 35 USC 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claims 1-8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. When determining that the enablement requirement ha not be met, the factors to be considered include but are not limited to:
1. the breadth of the claims
2. the nature of the invention,
3. The state of the prior art,
4. The level of one of ordinary skill,
5. The level of predictability in the art,
6. The amount of direction provided by the inventor,
7. The existence of working examples, and
8. The quantity of experimentation needed to make or use the invention based on the content of the disclosure.
The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention without under experimentation based on the lack of direction provided by inventor and lack of working examples. Claim 1 cites “analyzing and learning the tensor to generate a concept vector characterizing the symbol unit, wherein the concept vector is directly and consistently associated with the symbol unit.”, which lacks enablement. The claims nor the specification disclose what is meant by analyzing and learning the tensor to generate a concept vector, nor how a user or system would accomplish this. It does not give any examples of creating a concept vector from analyzing or learning a tensor, how the concept vector is different from a tensor as a vector is a first rank tensor, or what method or equation converts a tensor to a concept vector. Without any working examples and without any direction provided by inventor it would be left to a user to try and figure out what is meant by this limitation and how to accomplish it which constitutes undue experimentation, as such claim 1 is found to lack enablement. As claim 1 has been found to lack enablement, so too does claim 2-8 which depend on claim 1 but does not remedy the situation.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 cites the limitation “analyzing and learning the tensor to generate a concept vector characterizing the symbol unit” which renders the claim indefinite. Claim 1 starts with multimodal data, wherein features are extract and feature vectors are found, the feature vectors are fused or concatenated to form a tensor. Then a tensor is analyzed and learned to create a concept vector. However, the claims and specification fail to differentiate a tensor from the concept vector, define what exactly a concept vector is and how a concept vector is created from a tensor. A vector is a rank 1 tensor and the claims fuse vectors to form tensors, however it not clear how the concept vector is different from tensor, nor how it concept vector is found from the tensor. As such the concept vector is found to indefinite as it not clear to examiner what exactly the term is meant to encompass. In the interest of compact prosecution, the examiner interprets the concept vector to be tensor with any method performed on it. As claim 1 is rejected, so too are claims 2-8 which depend on claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2 and 4-8 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (“ChineseBert: Chinese Pretraining Enhanced by Glyph and Pinyin Information” – hereinafter Sun) in view of Guo (US 2008/0170788 A1) and further in view of Krishnan et al. (US 2023/0306087 A1 – hereinafter Krishnan).
In regards to claim 1, Sun discloses a method for characterizing cultural symbols by using a machine learning model, including: (Sun abstract cites “we propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining.”, wherein a language more is machine learning model and chine characters are cultural symbols.)
receiving multiple materials about a symbol unit, wherein the multiple materials at least include a picture drawing the symbol unit, (Sun abstract cites “The glyph embedding is obtained based on different fonts of a Chinese character, being able to capture character semantics from the visual features,” and page 2068 “Glyph Embedding” section cites “We follow Meng et al. (2019) to use three types of Chinese fonts – Fang-Song, XingKai and LiShu, each of which is instantiated as a 24x24 image with floating point pixels…”, these two teaches a glyph image of a Chinese character and thus is a picture of the symbol.) pronunciation of the symbol unit, (Sun abstract cites “the pinyin embedding characterizes the pronunciation of Chinese characters,…” , these teaches the pronunciation of the Chinese character or symbol.) and the symbol unit is a single cultural symbol or a combination of multiple cultural symbols; (Sun abstract teaches a system using glyph and pinyin information of Chinese characters wherein the Chinese characters are cultural symbols.)
for each material of the multiple materials, analyzing and learning the material to extract features of the material to form a set of feature vectors; (Sun page 2067 and section 3.1 cites “Figure 1 shows an overview of the proposed ChineseBERT model. For each Chinese character, its char embedding, glyph embedding and pinyin embedding are first concatenated…”, this is analyzing the features of the material to get feature vectors which are embeddings. Also see section 3.2 titles “glyph embedding” and “pinyin Embedding” that further teaches analyzing the materials to get feature vectors and embedding.)
fusing all of the formed feature vectors into a tensor; and (Sun page 2067 section 3.1 cites “Figure 1 shows an overview of the proposed ChineseBERT model. For each Chinese character, its char embedding, glyph embedding and pinyin embedding are first concatenated, and then mapped to a D-dimensional embedding through a fully connected layer to form the fusion embedding.” And page 2068 section 3.2 titled “Fusion Embedding” cites “Once we have the char embedding, the glyph embedding and the pinyin embedding for a character, we concatenate them to form a 3D-dimensional vector.”, These teaches fusing feature vectors into a tensor.)
However, Sun does not explicitly disclose an image or a video showing cultural meaning of the symbol unit, and analyzing and learning the tensor to generate a concept vector characterizing the symbol unit, wherein the concept vector is directly and consistently associated with the symbol unit.
Guo discloses an image or a video showing cultural meaning of the symbol unit. (Guo paragraph [0031] cites “ [0031] The animation program may be described as having three frames or scenes. A first scene presents an action or object, preferably a common object 10 such as the human ear shown in the static view of FIG. 1. A second scene ( or series of intermediate scenes) shows a transition or "morphing" of the object 10 into a Chinese character 12 that represents the object. The wavelike images 11 shown in the static view of FIG. 2 represent one or more intermediate frames of animation that gradually transition the image of the object 10 into the image of the character 12. A third scene, shown in FIG. 3, depicts the final static view of the Chinese character 12. A sound or syllable associated with the Chinese character may also be played from the DVD as an audible sound bite coincident with the animation to aid the student with pronunciation.”; this teaches a video with images of a Chinese character/symbol that morphs or changes into an image of the meaning of the Chinese character or symbol. For example, an ear in figure 1 morphs to the Chinese character for an ear in figure 3. It also teaches the video can have the pronunciation also.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of Sun with that of Guo in order to allow for combining the Chinese characters with an image of its meaning as both references deal with using Chinese characters and the benefit of doing so it allow for children to easier learn the meaning of Chinese characters and even the pronunciations as cited in para. [0028] of Guo wherein it cites “The present invention provides a novel and highly effective system and method for teaching young children to read, write, and speak Chinese.”.
However, Sun in view of Guo does not explicitly disclose analyzing and learning the tensor to generate a concept vector characterizing the symbol unit, wherein the concept vector is directly and consistently associated with the symbol unit.
Krishnan discloses analyzing and learning the tensor to generate a concept vector characterizing the symbol unit, wherein the concept vector is directly and consistently associated with the symbol unit. (Krishnan para. [0045] cites “Once the content of the templates 158 and/or icons 152, images 154 and illustrations 158 are converted to vector representations, they may be transmitted to a tensor generation unit 166 to be turned into tensors.”, this teaches analyzing and learning a tensor to get a concept vector. Para. [0046] cites “After tensors are generated, the tensors may be provided to a tensor summarization module 168 in order to increase the efficiency of the search and retrieval system. … This may involve creating one aggregate embedding for each of the different modalities in a tensor. For example, one aggregate embedding may be created for all the text content. Another one aggregate embedding may be generated for all the images and the like.” This also teaches combining vectors to form a tensor with prior citing we know tensors before vector representations which are the concept vector. Then para. [0047] teaches the summarized tensor is provided to index module for generating an asset index for each visual asset, thus the summarized tensor (concept vector) is associated with the symbol (character.).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of Sun in view of Guo with that of Krishnan in order to allow for generating a concept vector from a tensor as both Sun and Krishnan deal with multimodal fusion and vectors. It provides the benefit of using the summarized tensor in an index for querying and fast searching.
In regards to claim 2, Sun in view of Guo in view of Krishnan disclose the method of claim 1, wherein the tensor enables the machine learning model to infer one or two sets of feature vectors respectively corresponding to one or two materials in a picture drawing the symbol unit, pronunciation of the symbol unit, and an image or a video showing cultural meaning of the symbol unit, in the case that one or more further materials about the symbol unit are received by the machine learning model later, and the one or two materials are missing in the one or more further materials. (Krishnan para. [0049] teaches a search query can be entered in text or images and it will return similar content items or even a template having multimodal content such has text, images, icons and so on. Also, Krishnan para. [0164] teaches comparing the query embedding to a plurality of tensor representations, thus the text embedding or image embedding of query is compared to the multimodal tensor index, and returns multimodal data corresponding the tensor index. Thus, it can take text or image query and infer the other modals by matching it the multimodal data in the tensor index index.)
In regards to claim 4, Sun in view of Guo in view of Krishnan disclose the method of claim 1, wherein the single cultural symbol is a literal symbol, a mathematical symbol, a logical symbol, a trademark, a flag or a political symbol, and the combination of multiple cultural symbols is a literal word, a literal abbreviation, a mathematical formula or a logical representation. (Sun abstract teaches using Chinese characters which are literal symbols that make up literal words. Also see Sun page 2065 right column that teaches the Chinese characters are composed of radicals wherein the combination or radicals make the Chinese characters, which are words.)
In regards to claim 5, Sun in view of Guo in view of Krishnan disclose the method of claim 1, wherein the multiple materials further include a description of the cultural meaning of the symbol unit in a dictionary. (See Sun page 2065 right column that teaches the Chinese characters are composed of radicals wherein the combination or radicals make the Chinese characters, which are words, and the radicals also have semantic meaning which is a description of the symbol or character.)
In regards to claim 6, Sun in view of Guo in view of Krishnan disclose the method of claim 1, wherein the machine learning model is a deep learning model, a complete autoencoder, a undercomplete autoencoder, and/or a mathematical statistical model. (Sun abstract teaches using ChineseBERT, wherein BERT stands for Bidirectional Encoder Representations for Transformers which is an incomplete autoencoder. Also, Sun page 2066 section 2.2 teaches using deep neural network for learning glyph.)
In regards to claim 7, Sun in view of Guo in view of Krishnan disclose the method of claim 1, wherein the machine learning model is a single model for machine learning or a combination of multiple models for machine learning. (Sun figure 1 on page 2067 shows ChineseBert being a single machine learning model.
In regards to claim 8, Sun in view of Guo in view of Krishnan disclose the machine readable medium, having stored thereon instructions, that when executed by a machine, cause the machine to perform the method of claim 1. (Krishnan claim 20 teaches a non-transitory computer readable medium storing instructions.)
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (“ChineseBert: Chinese Pretraining Enhanced by Glyph and Pinyin Information” – hereinafter Sun) in view of Guo (US 2008/0170788 A1) in view of Krishnan et al. (US 2023/0306087 A1 – hereinafter Krishnan) and further in view of Peng et al. (“Fusing Phonetic Features and Chinese Character Representation for Sentiment Analysis” - hereinafter Peng).
In regards to claim 3, Sun in view of Guo in view of Krishnan disclose the method of claim 1, but does not explicitly disclose it further comprising:
fusing the concept vector and one or more concept vectors arranged in a specific order to form a further tensor, wherein the one or more concept vectors respectively characterize one or more other symbol units and are generated by the machine learning model, and the arrangement of the symbol unit and the one or more other symbol units in the specific order forms a context having a cultural meaning, and
analyzing and learning the further tensor to generate a further concept vector, wherein the further concept vector characterizes the characteristics of the symbol unit itself and the association of the symbol unit with the one or more other symbol units.
Peng discloses fusing the concept vector and one or more concept vectors arranged in a specific order to form a further tensor, wherein the one or more concept vectors respectively characterize one or more other symbol units and are generated by the machine learning model, and the arrangement of the symbol unit and the one or more other symbol units in the specific order forms a context having a cultural meaning, and analyzing and learning the further tensor to generate a further concept vector, wherein the further concept vector characterizes the characteristics of the symbol unit itself and the association of the symbol unit with the one or more other symbol units. (In light of the 112 supra the examiner interprets this claim to mean that vectors for a characters (words) in a sentence in a particular order are fused together. Peng page 157 section title “Pinyin with Intonation (PW)” teaches taking a sentence from text “‘今天心情好’ and converting it to Pinyin Sentences ‘jin1 tian1 xin1 qing2 hao3’. The pinyin characters or words are then embedding wherein features vectors are formed for each. Then in section 3.4 on page 157 it teaches feeding the sentence to a bidirectional long short-term memory (LSTM) network and getting a final representation of the sentence S from concatenation of the feature vectors in the forward and backward phase of the LSTM network. Also see section 3.5 wherein it teaches early fusion, wherein each Chinese character is concatenation of three segments, thus a tensor, and then sentence is a concatenation of the tensors. Section 3.5 also teaches late fusion at the sentence level.))
It would have been obvious to one of ordinary skill in the art before the earlies effective filing date of the claimed invention to modify the teachings of Sun in view of Guo in view of Krishnan with that of Peng in order to allow for fusing vectors in particular order or sequence as Sun, Guo and Peng all deal with multimodal data and vector fusion. The benefit of doing so it allows for sentencing modeling of Chinese characters wherein the particular order of characters can change the meaning or sentiment of the sentence.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127